Maintained by Difan Deng and Marius Lindauer.
The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.
Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.
Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.
1.
K, Venkata Ratna Prabha; Bindu, Chinni Hima; Devi, K Rama
An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile Journal Article
In: Biomedical Physics & Engineering Express, vol. 11, no. 4, pp. 045006, 2025.
@article{K_2025,
title = {An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile},
author = {Venkata Ratna Prabha K and Chinni Hima Bindu and K Rama Devi},
url = {https://dx.doi.org/10.1088/2057-1976/addbe7},
doi = {10.1088/2057-1976/addbe7},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Biomedical Physics & Engineering Express},
volume = {11},
number = {4},
pages = {045006},
publisher = {IOP Publishing},
abstract = {Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and intervention can reduce the severity of symptoms. Structural magnetic resonance imaging (sMRI) can improve diagnostic accuracy, facilitating early diagnosis to offer more tailored care. With the emergence of deep learning (DL), neuroimaging-based approaches for ASD diagnosis have been focused. However, many existing models lack interpretability of their decisions for diagnosis. The prime objective of this work is to perform ASD classification precisely and to interpret the classification process in a better way so as to discern the major features that are appropriate for the prediction of disorder. The proposed model employs neural architecture search network - mobile(NASNet-Mobile) model for ASD detection, which is integrated with an explainable artificial intelligence (XAI) technique called local interpretable model-agnostic explanations (LIME) for increased transparency of ASD classification. The model is trained on sMRI images of two age groups taken from autism brain imaging data exchange-I (ABIDE-I) dataset. The proposed model yielded accuracy of 0.9607, F1-score of 0.9614, specificity of 0.9774, sensitivity of 0.9451, negative predicted value (NPV) of 0.9429, positive predicted value (PPV) of 0.9783 and the diagnostic odds ratio of 745.59 for 2 to 11 years age group compared to 12 to 18 years group. These results are superior compared to other state of the art models Inception v3 and SqueezeNet.},
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Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and intervention can reduce the severity of symptoms. Structural magnetic resonance imaging (sMRI) can improve diagnostic accuracy, facilitating early diagnosis to offer more tailored care. With the emergence of deep learning (DL), neuroimaging-based approaches for ASD diagnosis have been focused. However, many existing models lack interpretability of their decisions for diagnosis. The prime objective of this work is to perform ASD classification precisely and to interpret the classification process in a better way so as to discern the major features that are appropriate for the prediction of disorder. The proposed model employs neural architecture search network - mobile(NASNet-Mobile) model for ASD detection, which is integrated with an explainable artificial intelligence (XAI) technique called local interpretable model-agnostic explanations (LIME) for increased transparency of ASD classification. The model is trained on sMRI images of two age groups taken from autism brain imaging data exchange-I (ABIDE-I) dataset. The proposed model yielded accuracy of 0.9607, F1-score of 0.9614, specificity of 0.9774, sensitivity of 0.9451, negative predicted value (NPV) of 0.9429, positive predicted value (PPV) of 0.9783 and the diagnostic odds ratio of 745.59 for 2 to 11 years age group compared to 12 to 18 years group. These results are superior compared to other state of the art models Inception v3 and SqueezeNet.
2.
Hu, Mengxiang; Li, Junchi; Dong, Yongquan; Zhang, Zichen; Liu, Weifan; Zhang, Peilin; Ping, Yuchao; Jiang, Le; Yu, Zekuan
Mixed-GGNAS: Mixed Search-space NAS based on genetic algorithm combined with gradient descent for medical image segmentation Journal Article
In: Expert Systems with Applications, vol. 289, pp. 128338, 2025, ISSN: 0957-4174.
@article{HU2025128338,
title = {Mixed-GGNAS: Mixed Search-space NAS based on genetic algorithm combined with gradient descent for medical image segmentation},
author = {Mengxiang Hu and Junchi Li and Yongquan Dong and Zichen Zhang and Weifan Liu and Peilin Zhang and Yuchao Ping and Le Jiang and Zekuan Yu},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425019578},
doi = {https://doi.org/10.1016/j.eswa.2025.128338},
issn = {0957-4174},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Expert Systems with Applications},
volume = {289},
pages = {128338},
abstract = {Medical images segmentation is a pivotal procedure, playing a fundamental role in computer-assisted diagnosis and treatment. Despite the significant advancements in methods leveraging deep learning for this purpose, many networks still face challenges related to efficiency, often requiring substantial time and manual efforts. Neural architecture search (NAS) has gained considerable attention in the automated design of neural networks. This study introduces a new NAS method, Mixed-GGNAS, a Mixed Search-space NAS method based on Genetic algorithm combined with Gradient descent. Our approach creatively combines manually designed network blocks with DARTS blocks to construct a mixed search space. We then employ a method that integrates genetic algorithms and gradient descent to concurrently search for both block types and internal operations within the block. Within a U-shaped network framework, we propose a Multi-feature fusion strategy based on Vision Transformer (ViT) and search for hyperparameters of it. Additionally, we employ a Multi-scale mixed loss function to enhance the model’s ability to learn features at various scales. Experimental results demonstrate that the proposed approach outperforms or is comparable to the state-of-the-art NAS methods and manually designed Networks. Ablation studies conducted on two datasets further validate the method’s efficacy in enhancing model performance. The code is available at https://github.com/Hmxki/Mixed-GGNAS.},
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Medical images segmentation is a pivotal procedure, playing a fundamental role in computer-assisted diagnosis and treatment. Despite the significant advancements in methods leveraging deep learning for this purpose, many networks still face challenges related to efficiency, often requiring substantial time and manual efforts. Neural architecture search (NAS) has gained considerable attention in the automated design of neural networks. This study introduces a new NAS method, Mixed-GGNAS, a Mixed Search-space NAS method based on Genetic algorithm combined with Gradient descent. Our approach creatively combines manually designed network blocks with DARTS blocks to construct a mixed search space. We then employ a method that integrates genetic algorithms and gradient descent to concurrently search for both block types and internal operations within the block. Within a U-shaped network framework, we propose a Multi-feature fusion strategy based on Vision Transformer (ViT) and search for hyperparameters of it. Additionally, we employ a Multi-scale mixed loss function to enhance the model’s ability to learn features at various scales. Experimental results demonstrate that the proposed approach outperforms or is comparable to the state-of-the-art NAS methods and manually designed Networks. Ablation studies conducted on two datasets further validate the method’s efficacy in enhancing model performance. The code is available at https://github.com/Hmxki/Mixed-GGNAS.
3.
Sun, Yu; Zhang, Xianglin; Dong, Liang; Liu, Ning
In: Applied Soft Computing, vol. 179, pp. 113279, 2025, ISSN: 1568-4946.
@article{SUN2025113279,
title = {Multi-objective evolutionary neural architecture search for medical image analysis using transformer and large language models in advancing public health},
author = {Yu Sun and Xianglin Zhang and Liang Dong and Ning Liu},
url = {https://www.sciencedirect.com/science/article/pii/S1568494625005903},
doi = {https://doi.org/10.1016/j.asoc.2025.113279},
issn = {1568-4946},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Applied Soft Computing},
volume = {179},
pages = {113279},
abstract = {The rapid growth of medical imaging data in modern healthcare networks demands sophisticated automated analysis methods that can maintain high accuracy while operating efficiently at scale. Current approaches using transformers and large language models (LLMs) face challenges balancing computational requirements with diagnostic precision across diverse healthcare settings. This paper presents TransMed-NAS (transformer medical neural architecture search), a multi-objective evolutionary neural architecture search framework that automatically discovers efficient hybrid architectures by integrating transformers and LLMs for medical image segmentation. Our approach leverages evolutionary computation to optimize segmentation accuracy and computational efficiency while incorporating medical domain knowledge through LLM guidance. The framework introduces several innovations: a hierarchical channel selection strategy that preserves clinically relevant features, a weight entanglement mechanism that accelerates architecture search through intelligent knowledge transfer, and a surrogate model acceleration technique that reduces computational overhead while maintaining reliability. Experimental results on the ISIC 2020 dataset demonstrate TransMed-NAS’s superior performance compared to state-of-the-art methods. Our small model variant achieves competitive accuracy (0.934 Dice score) with only 0.82M parameters, while our large variant establishes new benchmarks (0.947 Dice score) with significantly reduced computational requirements. Ablation studies confirm the effectiveness of each component, particularly highlighting how LLM integration enhances architecture search efficiency and clinical relevance. These results demonstrate TransMed-NAS’s potential to advance automated medical image analysis in resource-diverse healthcare settings, making sophisticated diagnostic capabilities more accessible to underserved communities.},
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The rapid growth of medical imaging data in modern healthcare networks demands sophisticated automated analysis methods that can maintain high accuracy while operating efficiently at scale. Current approaches using transformers and large language models (LLMs) face challenges balancing computational requirements with diagnostic precision across diverse healthcare settings. This paper presents TransMed-NAS (transformer medical neural architecture search), a multi-objective evolutionary neural architecture search framework that automatically discovers efficient hybrid architectures by integrating transformers and LLMs for medical image segmentation. Our approach leverages evolutionary computation to optimize segmentation accuracy and computational efficiency while incorporating medical domain knowledge through LLM guidance. The framework introduces several innovations: a hierarchical channel selection strategy that preserves clinically relevant features, a weight entanglement mechanism that accelerates architecture search through intelligent knowledge transfer, and a surrogate model acceleration technique that reduces computational overhead while maintaining reliability. Experimental results on the ISIC 2020 dataset demonstrate TransMed-NAS’s superior performance compared to state-of-the-art methods. Our small model variant achieves competitive accuracy (0.934 Dice score) with only 0.82M parameters, while our large variant establishes new benchmarks (0.947 Dice score) with significantly reduced computational requirements. Ablation studies confirm the effectiveness of each component, particularly highlighting how LLM integration enhances architecture search efficiency and clinical relevance. These results demonstrate TransMed-NAS’s potential to advance automated medical image analysis in resource-diverse healthcare settings, making sophisticated diagnostic capabilities more accessible to underserved communities.
4.
Zechen, Zheng; Xuelei, He; Fengjun, Zhao; Xiaowei, He
PSNAS-Net: Hybrid gradient-physical optimizationfor efficient neural architecture search in customized medical imaging analysis Journal Article
In: Expert Systems with Applications, vol. 288, pp. 128155, 2025, ISSN: 0957-4174.
@article{ZECHEN2025128155,
title = {PSNAS-Net: Hybrid gradient-physical optimizationfor efficient neural architecture search in customized medical imaging analysis},
author = {Zheng Zechen and He Xuelei and Zhao Fengjun and He Xiaowei},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425017750},
doi = {https://doi.org/10.1016/j.eswa.2025.128155},
issn = {0957-4174},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Expert Systems with Applications},
volume = {288},
pages = {128155},
abstract = {Neural architecture search (NAS) facilitates the automated construction of neural networks tailored to specific tasks and requirements, resulting in models that are more closely aligned with the target task’s demands. However, in many studies, the extensive design space, high search costs, and time-consuming evaluation calculations render NAS impractical for numerous medical data tasks. Addressing these challenges, this study introduces an efficient algorithm for searching deep learning architectures. Initially, we propose 19 fundamental rules to streamline the design space, thereby reducing its scale. To improve the efficiency of the algorithm, we designed a NAS framework (PSNAS-Net) for convolutional neural networks and VisionTransformer, which consists of two search stages: Firstly, the improved powell algorithm is used to determine the model range, and the population-based simulated annealing algorithm is utilized to expedite the search for the final model. During the neural architecture search process, we consider accuracy, parameters, FLOPs, and model stability as comprehensive evaluation objectives, we designed a robust, flexible, and comprehensive metric for model evaluation. The experimental results demonstrate that PSNAS-Net achieves significantly shorter search times (0.05-1.47 GPU Days) compared to 19 existing NAS methods, while discovering compact models (as small as 0.11M) with superior performance across five medical image benchmarks. This study offers a viable approach for model search that accommodates individualized requirements.},
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Neural architecture search (NAS) facilitates the automated construction of neural networks tailored to specific tasks and requirements, resulting in models that are more closely aligned with the target task’s demands. However, in many studies, the extensive design space, high search costs, and time-consuming evaluation calculations render NAS impractical for numerous medical data tasks. Addressing these challenges, this study introduces an efficient algorithm for searching deep learning architectures. Initially, we propose 19 fundamental rules to streamline the design space, thereby reducing its scale. To improve the efficiency of the algorithm, we designed a NAS framework (PSNAS-Net) for convolutional neural networks and VisionTransformer, which consists of two search stages: Firstly, the improved powell algorithm is used to determine the model range, and the population-based simulated annealing algorithm is utilized to expedite the search for the final model. During the neural architecture search process, we consider accuracy, parameters, FLOPs, and model stability as comprehensive evaluation objectives, we designed a robust, flexible, and comprehensive metric for model evaluation. The experimental results demonstrate that PSNAS-Net achieves significantly shorter search times (0.05-1.47 GPU Days) compared to 19 existing NAS methods, while discovering compact models (as small as 0.11M) with superior performance across five medical image benchmarks. This study offers a viable approach for model search that accommodates individualized requirements.
5.
Becktepe, Jannis; Hennig, Leona; Oeltze-Jafra, Steffen; Lindauer, Marius
Auto-nnU-Net: Towards Automated Medical Image Segmentation Technical Report
2025.
@techreport{becktepe2025autonnunetautomatedmedicalimage,
title = {Auto-nnU-Net: Towards Automated Medical Image Segmentation},
author = {Jannis Becktepe and Leona Hennig and Steffen Oeltze-Jafra and Marius Lindauer},
url = {https://arxiv.org/abs/2505.16561},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
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6.
Ardila, Diego Páez; Carvalho, Thiago; Saavedra, Santiago Vasquez; Niño, Cesar Valencia; Figueiredo, Karla; Vellasco, Marley
Quantum-Inspired NAS With Attention-Based Search Spaces in Medical Applications Proceedings Article
In: 2025 IEEE Symposium on Computational Intelligence in Health and Medicine Companion (CIHM Companion), pp. 1-5, 2025.
@inproceedings{11002695,
title = {Quantum-Inspired NAS With Attention-Based Search Spaces in Medical Applications},
author = {Diego Páez Ardila and Thiago Carvalho and Santiago Vasquez Saavedra and Cesar Valencia Niño and Karla Figueiredo and Marley Vellasco},
doi = {10.1109/CIHMCompanion65205.2025.11002695},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {2025 IEEE Symposium on Computational Intelligence in Health and Medicine Companion (CIHM Companion)},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
7.
Cao, Bin; Deng, Huanyu; Hao, Yiming; Luo, Xiao
Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation Journal Article
In: Information Fusion, vol. 123, pp. 103301, 2025, ISSN: 1566-2535.
@article{CAO2025103301,
title = {Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation},
author = {Bin Cao and Huanyu Deng and Yiming Hao and Xiao Luo},
url = {https://www.sciencedirect.com/science/article/pii/S1566253525003744},
doi = {https://doi.org/10.1016/j.inffus.2025.103301},
issn = {1566-2535},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Information Fusion},
volume = {123},
pages = {103301},
abstract = {With the rapid development of artificial intelligence, medical image semantic segmentation is being used more widely. However, centralized training can lead to privacy risks. At the same time, MRI provides multiple views that together describe the anatomical structure of a lesion, but a single view may not fully capture all features. Therefore, integrating multi-view information in a federated learning setting is a key challenge for improving model generalization. This study combines federated learning, neural architecture search (NAS) and data fusion techniques to address issues related to data privacy, cross-institutional data distribution differences and multi-view information fusion in medical imaging. To achieve this, we propose the FL-MONAS framework, which leverages the advantages of NAS and federated learning. It uses a Pareto-frontier-based multi-objective optimization strategy to effectively combine 2D MRI with 3D anatomical structures, improving model performance while ensuring data privacy. Experimental results show that FL-MONAS maintains strong segmentation performance even in non-IID scenarios, providing an efficient and privacy-friendly solution for medical image analysis.},
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With the rapid development of artificial intelligence, medical image semantic segmentation is being used more widely. However, centralized training can lead to privacy risks. At the same time, MRI provides multiple views that together describe the anatomical structure of a lesion, but a single view may not fully capture all features. Therefore, integrating multi-view information in a federated learning setting is a key challenge for improving model generalization. This study combines federated learning, neural architecture search (NAS) and data fusion techniques to address issues related to data privacy, cross-institutional data distribution differences and multi-view information fusion in medical imaging. To achieve this, we propose the FL-MONAS framework, which leverages the advantages of NAS and federated learning. It uses a Pareto-frontier-based multi-objective optimization strategy to effectively combine 2D MRI with 3D anatomical structures, improving model performance while ensuring data privacy. Experimental results show that FL-MONAS maintains strong segmentation performance even in non-IID scenarios, providing an efficient and privacy-friendly solution for medical image analysis.
8.
Mecharbat, Lotfi Abdelkrim; Almakky, Ibrahim; Takac, Martin; Yaqub, Mohammad
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search Technical Report
2025.
@techreport{mecharbat2025mednnssupernetbasedmedicaltaskadaptiveb,
title = {MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search},
author = {Lotfi Abdelkrim Mecharbat and Ibrahim Almakky and Martin Takac and Mohammad Yaqub},
url = {https://arxiv.org/abs/2504.15865},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
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9.
Wani, M. Arif; Sultan, Bisma; Ali, Sarwat; Sofi, Mukhtar Ahmad
Introduction to Deep Learning Applications Book Chapter
In: Advances in Deep Learning, Volume 2, pp. 1–14, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-3498-9.
@inbook{Wani2025c,
title = {Introduction to Deep Learning Applications},
author = {M. Arif Wani and Bisma Sultan and Sarwat Ali and Mukhtar Ahmad Sofi},
url = {https://doi.org/10.1007/978-981-96-3498-9_1},
doi = {10.1007/978-981-96-3498-9_1},
isbn = {978-981-96-3498-9},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Advances in Deep Learning, Volume 2},
pages = {1–14},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep learning has emerged as a cornerstone of modern Artificial Intelligence (AI), offering unparalleled performance across a variety of complex tasks. Its ability to automatically learn features from vast amounts of data has transformed industries ranging from computer vision to natural language processing. Among the numerous applications of deep learning, three areas have witnessed significant progress: Neural Architecture Search (NAS), steganography, and medical applications. This chapter explores application of deep learning architectures in these three areas.},
keywords = {},
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Deep learning has emerged as a cornerstone of modern Artificial Intelligence (AI), offering unparalleled performance across a variety of complex tasks. Its ability to automatically learn features from vast amounts of data has transformed industries ranging from computer vision to natural language processing. Among the numerous applications of deep learning, three areas have witnessed significant progress: Neural Architecture Search (NAS), steganography, and medical applications. This chapter explores application of deep learning architectures in these three areas.
10.
Guarrasi, Valerio; Mogensen, Klara; Tassinari, Sara; Qvarlander, Sara; Soda, Paolo
Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging Technical Report
2025.
@techreport{guarrasi2025timingeverythingfindingoptimal,
title = {Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging},
author = {Valerio Guarrasi and Klara Mogensen and Sara Tassinari and Sara Qvarlander and Paolo Soda},
url = {https://arxiv.org/abs/2505.02467},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
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11.
Mecharbat, Lotfi Abdelkrim; Almakky, Ibrahim; Takac, Martin; Yaqub, Mohammad
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search Technical Report
2025.
@techreport{mecharbat2025mednnssupernetbasedmedicaltaskadaptive,
title = {MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search},
author = {Lotfi Abdelkrim Mecharbat and Ibrahim Almakky and Martin Takac and Mohammad Yaqub},
url = {https://arxiv.org/abs/2504.15865},
year = {2025},
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urldate = {2025-01-01},
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12.
Yu, Jiandong; Li, Tongtong; Shi, Xuerong; Zhao, Ziyang; Chen, Miao; Zhang, Yu; Wang, Junyu; Yao, Zhijun; Fang, Lei; Hu, Bin
ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification Journal Article
In: Biomedical Signal Processing and Control, vol. 104, pp. 107479, 2025, ISSN: 1746-8094.
@article{YU2025107479,
title = {ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification},
author = {Jiandong Yu and Tongtong Li and Xuerong Shi and Ziyang Zhao and Miao Chen and Yu Zhang and Junyu Wang and Zhijun Yao and Lei Fang and Bin Hu},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424015374},
doi = {https://doi.org/10.1016/j.bspc.2024.107479},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {104},
pages = {107479},
abstract = {Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.},
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Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.
13.
Wang, Weibo; Li, Hua
NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search Journal Article
In: Biomedical Signal Processing and Control, vol. 100, pp. 107022, 2025, ISSN: 1746-8094.
@article{WANG2025107022,
title = {NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search},
author = {Weibo Wang and Hua Li},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424010802},
doi = {https://doi.org/10.1016/j.bspc.2024.107022},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {100},
pages = {107022},
abstract = {In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.},
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tppubtype = {article}
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In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.
14.
AL-Sabri, Raeed; Gao, Jianliang; Chen, Jiamin; Oloulade, Babatounde Moctard; Wu, Zhenpeng; Abdullah, Monir; Hu, Xiaohua
M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions Proceedings Article
In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1783-1788, IEEE Computer Society, Los Alamitos, CA, USA, 2024.
@inproceedings{10821927,
title = { M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions },
author = {Raeed AL-Sabri and Jianliang Gao and Jiamin Chen and Babatounde Moctard Oloulade and Zhenpeng Wu and Monir Abdullah and Xiaohua Hu},
url = {https://doi.ieeecomputersociety.org/10.1109/BIBM62325.2024.10821927},
doi = {10.1109/BIBM62325.2024.10821927},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
booktitle = {2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
pages = {1783-1788},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.
15.
(Ed.)
Medical Neural Architecture Search: Survey and Taxonomy Collection
2024.
@collection{Benmeziane-ijcai24a,
title = {Medical Neural Architecture Search: Survey and Taxonomy},
author = {Hadjer Benmeziane and Imane Hamzaoui and Kaoutar El Maghraoui and Kaoutar El Maghraoui},
url = {https://www.ijcai.org/proceedings/2024/0878.pdf},
year = {2024},
date = {2024-08-03},
urldate = {2024-08-03},
booktitle = {IJCAI 2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
16.
Zhang, Jinnian; Chen, Weijie; Joshi, Tanmayee; Uyanik, Meltem; Zhang, Xiaomin; Loh, Po-Ling; Jog, Varun; Bruce, Richard; Garrett, John; McMillan, Alan
RobMedNAS: searching robust neural network architectures for medical image synthesis Journal Article
In: Biomedical Physics & Engineering Express, vol. 10, no. 5, pp. 055029, 2024.
@article{Zhang_2024,
title = {RobMedNAS: searching robust neural network architectures for medical image synthesis},
author = {Jinnian Zhang and Weijie Chen and Tanmayee Joshi and Meltem Uyanik and Xiaomin Zhang and Po-Ling Loh and Varun Jog and Richard Bruce and John Garrett and Alan McMillan},
url = {https://dx.doi.org/10.1088/2057-1976/ad6e87},
doi = {10.1088/2057-1976/ad6e87},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Biomedical Physics & Engineering Express},
volume = {10},
number = {5},
pages = {055029},
publisher = {IOP Publishing},
abstract = {Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.},
keywords = {},
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Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.
17.
Asiimwe, Arnold; Das, William; Benmeziane, Hadjer; Maghraoui, Kaoutar El
EDGE2024, 2024.
@conference{Asiimwe-edge24a,
title = {EfficientMedSAM: Accelerating Medical Image Segmentation via Neural Architecture Search and Knowledge Distillation},
author = {Arnold Asiimwe and William Das and Hadjer Benmeziane and Kaoutar El Maghraoui},
url = {https://research.ibm.com/publications/efficientmedsam-accelerating-medical-image-segmentation-via-neural-architecture-search-and-knowledge-distillation},
year = {2024},
date = {2024-07-07},
urldate = {2024-07-07},
booktitle = {EDGE2024},
journal = {EDGE 2024},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
18.
Berezsky, O. M.; Liashchynskyi, P. B.
METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS Journal Article
In: Radio Electronics, Computer Science, Control, no. 1, pp. 104, 2024.
@article{Berezsky_Liashchynskyi_2024,
title = {METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS},
author = {O. M. Berezsky and P. B. Liashchynskyi},
url = {http://ric.zntu.edu.ua/article/view/300976},
doi = {10.15588/1607-3274-2024-1-10},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Radio Electronics, Computer Science, Control},
number = {1},
pages = {104},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
19.
Rajesh, Chilukamari; Sadam, Ravichandra; Kumar, Sushil
Automated Deep Learning Models for Medical Image Segmentation and Denoising Proceedings Article
In: 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), pp. 1-7, 2024.
@inproceedings{10815837,
title = {Automated Deep Learning Models for Medical Image Segmentation and Denoising},
author = {Chilukamari Rajesh and Ravichandra Sadam and Sushil Kumar},
url = {https://ieeexplore.ieee.org/abstract/document/10815837},
doi = {10.1109/ICSPCS63175.2024.10815837},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 17th International Conference on Signal Processing and Communication System (ICSPCS)},
pages = {1-7},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
20.
Franco-Gaona, Erick; Avila-Garcia, Maria-Susana; Cruz-Aceves, Ivan; Orocio-Garcia, Hiram-Efrain; Escobedo-Gordillo, Andres; Brieva, Jorge
Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis Proceedings Article
In: 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), pp. 1-4, 2024.
@inproceedings{10783513,
title = {Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis},
author = {Erick Franco-Gaona and Maria-Susana Avila-Garcia and Ivan Cruz-Aceves and Hiram-Efrain Orocio-Garcia and Andres Escobedo-Gordillo and Jorge Brieva},
url = {https://ieeexplore.ieee.org/abstract/document/10783513},
doi = {10.1109/SIPAIM62974.2024.10783513},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)},
pages = {1-4},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2025
148.
K, Venkata Ratna Prabha; Bindu, Chinni Hima; Devi, K Rama
An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile Journal Article
In: Biomedical Physics & Engineering Express, vol. 11, no. 4, pp. 045006, 2025.
@article{K_2025,
title = {An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile},
author = {Venkata Ratna Prabha K and Chinni Hima Bindu and K Rama Devi},
url = {https://dx.doi.org/10.1088/2057-1976/addbe7},
doi = {10.1088/2057-1976/addbe7},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Biomedical Physics & Engineering Express},
volume = {11},
number = {4},
pages = {045006},
publisher = {IOP Publishing},
abstract = {Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and intervention can reduce the severity of symptoms. Structural magnetic resonance imaging (sMRI) can improve diagnostic accuracy, facilitating early diagnosis to offer more tailored care. With the emergence of deep learning (DL), neuroimaging-based approaches for ASD diagnosis have been focused. However, many existing models lack interpretability of their decisions for diagnosis. The prime objective of this work is to perform ASD classification precisely and to interpret the classification process in a better way so as to discern the major features that are appropriate for the prediction of disorder. The proposed model employs neural architecture search network - mobile(NASNet-Mobile) model for ASD detection, which is integrated with an explainable artificial intelligence (XAI) technique called local interpretable model-agnostic explanations (LIME) for increased transparency of ASD classification. The model is trained on sMRI images of two age groups taken from autism brain imaging data exchange-I (ABIDE-I) dataset. The proposed model yielded accuracy of 0.9607, F1-score of 0.9614, specificity of 0.9774, sensitivity of 0.9451, negative predicted value (NPV) of 0.9429, positive predicted value (PPV) of 0.9783 and the diagnostic odds ratio of 745.59 for 2 to 11 years age group compared to 12 to 18 years group. These results are superior compared to other state of the art models Inception v3 and SqueezeNet.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and intervention can reduce the severity of symptoms. Structural magnetic resonance imaging (sMRI) can improve diagnostic accuracy, facilitating early diagnosis to offer more tailored care. With the emergence of deep learning (DL), neuroimaging-based approaches for ASD diagnosis have been focused. However, many existing models lack interpretability of their decisions for diagnosis. The prime objective of this work is to perform ASD classification precisely and to interpret the classification process in a better way so as to discern the major features that are appropriate for the prediction of disorder. The proposed model employs neural architecture search network - mobile(NASNet-Mobile) model for ASD detection, which is integrated with an explainable artificial intelligence (XAI) technique called local interpretable model-agnostic explanations (LIME) for increased transparency of ASD classification. The model is trained on sMRI images of two age groups taken from autism brain imaging data exchange-I (ABIDE-I) dataset. The proposed model yielded accuracy of 0.9607, F1-score of 0.9614, specificity of 0.9774, sensitivity of 0.9451, negative predicted value (NPV) of 0.9429, positive predicted value (PPV) of 0.9783 and the diagnostic odds ratio of 745.59 for 2 to 11 years age group compared to 12 to 18 years group. These results are superior compared to other state of the art models Inception v3 and SqueezeNet.
147.
Hu, Mengxiang; Li, Junchi; Dong, Yongquan; Zhang, Zichen; Liu, Weifan; Zhang, Peilin; Ping, Yuchao; Jiang, Le; Yu, Zekuan
Mixed-GGNAS: Mixed Search-space NAS based on genetic algorithm combined with gradient descent for medical image segmentation Journal Article
In: Expert Systems with Applications, vol. 289, pp. 128338, 2025, ISSN: 0957-4174.
@article{HU2025128338,
title = {Mixed-GGNAS: Mixed Search-space NAS based on genetic algorithm combined with gradient descent for medical image segmentation},
author = {Mengxiang Hu and Junchi Li and Yongquan Dong and Zichen Zhang and Weifan Liu and Peilin Zhang and Yuchao Ping and Le Jiang and Zekuan Yu},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425019578},
doi = {https://doi.org/10.1016/j.eswa.2025.128338},
issn = {0957-4174},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Expert Systems with Applications},
volume = {289},
pages = {128338},
abstract = {Medical images segmentation is a pivotal procedure, playing a fundamental role in computer-assisted diagnosis and treatment. Despite the significant advancements in methods leveraging deep learning for this purpose, many networks still face challenges related to efficiency, often requiring substantial time and manual efforts. Neural architecture search (NAS) has gained considerable attention in the automated design of neural networks. This study introduces a new NAS method, Mixed-GGNAS, a Mixed Search-space NAS method based on Genetic algorithm combined with Gradient descent. Our approach creatively combines manually designed network blocks with DARTS blocks to construct a mixed search space. We then employ a method that integrates genetic algorithms and gradient descent to concurrently search for both block types and internal operations within the block. Within a U-shaped network framework, we propose a Multi-feature fusion strategy based on Vision Transformer (ViT) and search for hyperparameters of it. Additionally, we employ a Multi-scale mixed loss function to enhance the model’s ability to learn features at various scales. Experimental results demonstrate that the proposed approach outperforms or is comparable to the state-of-the-art NAS methods and manually designed Networks. Ablation studies conducted on two datasets further validate the method’s efficacy in enhancing model performance. The code is available at https://github.com/Hmxki/Mixed-GGNAS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Medical images segmentation is a pivotal procedure, playing a fundamental role in computer-assisted diagnosis and treatment. Despite the significant advancements in methods leveraging deep learning for this purpose, many networks still face challenges related to efficiency, often requiring substantial time and manual efforts. Neural architecture search (NAS) has gained considerable attention in the automated design of neural networks. This study introduces a new NAS method, Mixed-GGNAS, a Mixed Search-space NAS method based on Genetic algorithm combined with Gradient descent. Our approach creatively combines manually designed network blocks with DARTS blocks to construct a mixed search space. We then employ a method that integrates genetic algorithms and gradient descent to concurrently search for both block types and internal operations within the block. Within a U-shaped network framework, we propose a Multi-feature fusion strategy based on Vision Transformer (ViT) and search for hyperparameters of it. Additionally, we employ a Multi-scale mixed loss function to enhance the model’s ability to learn features at various scales. Experimental results demonstrate that the proposed approach outperforms or is comparable to the state-of-the-art NAS methods and manually designed Networks. Ablation studies conducted on two datasets further validate the method’s efficacy in enhancing model performance. The code is available at https://github.com/Hmxki/Mixed-GGNAS.
146.
Sun, Yu; Zhang, Xianglin; Dong, Liang; Liu, Ning
In: Applied Soft Computing, vol. 179, pp. 113279, 2025, ISSN: 1568-4946.
@article{SUN2025113279,
title = {Multi-objective evolutionary neural architecture search for medical image analysis using transformer and large language models in advancing public health},
author = {Yu Sun and Xianglin Zhang and Liang Dong and Ning Liu},
url = {https://www.sciencedirect.com/science/article/pii/S1568494625005903},
doi = {https://doi.org/10.1016/j.asoc.2025.113279},
issn = {1568-4946},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Applied Soft Computing},
volume = {179},
pages = {113279},
abstract = {The rapid growth of medical imaging data in modern healthcare networks demands sophisticated automated analysis methods that can maintain high accuracy while operating efficiently at scale. Current approaches using transformers and large language models (LLMs) face challenges balancing computational requirements with diagnostic precision across diverse healthcare settings. This paper presents TransMed-NAS (transformer medical neural architecture search), a multi-objective evolutionary neural architecture search framework that automatically discovers efficient hybrid architectures by integrating transformers and LLMs for medical image segmentation. Our approach leverages evolutionary computation to optimize segmentation accuracy and computational efficiency while incorporating medical domain knowledge through LLM guidance. The framework introduces several innovations: a hierarchical channel selection strategy that preserves clinically relevant features, a weight entanglement mechanism that accelerates architecture search through intelligent knowledge transfer, and a surrogate model acceleration technique that reduces computational overhead while maintaining reliability. Experimental results on the ISIC 2020 dataset demonstrate TransMed-NAS’s superior performance compared to state-of-the-art methods. Our small model variant achieves competitive accuracy (0.934 Dice score) with only 0.82M parameters, while our large variant establishes new benchmarks (0.947 Dice score) with significantly reduced computational requirements. Ablation studies confirm the effectiveness of each component, particularly highlighting how LLM integration enhances architecture search efficiency and clinical relevance. These results demonstrate TransMed-NAS’s potential to advance automated medical image analysis in resource-diverse healthcare settings, making sophisticated diagnostic capabilities more accessible to underserved communities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rapid growth of medical imaging data in modern healthcare networks demands sophisticated automated analysis methods that can maintain high accuracy while operating efficiently at scale. Current approaches using transformers and large language models (LLMs) face challenges balancing computational requirements with diagnostic precision across diverse healthcare settings. This paper presents TransMed-NAS (transformer medical neural architecture search), a multi-objective evolutionary neural architecture search framework that automatically discovers efficient hybrid architectures by integrating transformers and LLMs for medical image segmentation. Our approach leverages evolutionary computation to optimize segmentation accuracy and computational efficiency while incorporating medical domain knowledge through LLM guidance. The framework introduces several innovations: a hierarchical channel selection strategy that preserves clinically relevant features, a weight entanglement mechanism that accelerates architecture search through intelligent knowledge transfer, and a surrogate model acceleration technique that reduces computational overhead while maintaining reliability. Experimental results on the ISIC 2020 dataset demonstrate TransMed-NAS’s superior performance compared to state-of-the-art methods. Our small model variant achieves competitive accuracy (0.934 Dice score) with only 0.82M parameters, while our large variant establishes new benchmarks (0.947 Dice score) with significantly reduced computational requirements. Ablation studies confirm the effectiveness of each component, particularly highlighting how LLM integration enhances architecture search efficiency and clinical relevance. These results demonstrate TransMed-NAS’s potential to advance automated medical image analysis in resource-diverse healthcare settings, making sophisticated diagnostic capabilities more accessible to underserved communities.
145.
Zechen, Zheng; Xuelei, He; Fengjun, Zhao; Xiaowei, He
PSNAS-Net: Hybrid gradient-physical optimizationfor efficient neural architecture search in customized medical imaging analysis Journal Article
In: Expert Systems with Applications, vol. 288, pp. 128155, 2025, ISSN: 0957-4174.
@article{ZECHEN2025128155,
title = {PSNAS-Net: Hybrid gradient-physical optimizationfor efficient neural architecture search in customized medical imaging analysis},
author = {Zheng Zechen and He Xuelei and Zhao Fengjun and He Xiaowei},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425017750},
doi = {https://doi.org/10.1016/j.eswa.2025.128155},
issn = {0957-4174},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Expert Systems with Applications},
volume = {288},
pages = {128155},
abstract = {Neural architecture search (NAS) facilitates the automated construction of neural networks tailored to specific tasks and requirements, resulting in models that are more closely aligned with the target task’s demands. However, in many studies, the extensive design space, high search costs, and time-consuming evaluation calculations render NAS impractical for numerous medical data tasks. Addressing these challenges, this study introduces an efficient algorithm for searching deep learning architectures. Initially, we propose 19 fundamental rules to streamline the design space, thereby reducing its scale. To improve the efficiency of the algorithm, we designed a NAS framework (PSNAS-Net) for convolutional neural networks and VisionTransformer, which consists of two search stages: Firstly, the improved powell algorithm is used to determine the model range, and the population-based simulated annealing algorithm is utilized to expedite the search for the final model. During the neural architecture search process, we consider accuracy, parameters, FLOPs, and model stability as comprehensive evaluation objectives, we designed a robust, flexible, and comprehensive metric for model evaluation. The experimental results demonstrate that PSNAS-Net achieves significantly shorter search times (0.05-1.47 GPU Days) compared to 19 existing NAS methods, while discovering compact models (as small as 0.11M) with superior performance across five medical image benchmarks. This study offers a viable approach for model search that accommodates individualized requirements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural architecture search (NAS) facilitates the automated construction of neural networks tailored to specific tasks and requirements, resulting in models that are more closely aligned with the target task’s demands. However, in many studies, the extensive design space, high search costs, and time-consuming evaluation calculations render NAS impractical for numerous medical data tasks. Addressing these challenges, this study introduces an efficient algorithm for searching deep learning architectures. Initially, we propose 19 fundamental rules to streamline the design space, thereby reducing its scale. To improve the efficiency of the algorithm, we designed a NAS framework (PSNAS-Net) for convolutional neural networks and VisionTransformer, which consists of two search stages: Firstly, the improved powell algorithm is used to determine the model range, and the population-based simulated annealing algorithm is utilized to expedite the search for the final model. During the neural architecture search process, we consider accuracy, parameters, FLOPs, and model stability as comprehensive evaluation objectives, we designed a robust, flexible, and comprehensive metric for model evaluation. The experimental results demonstrate that PSNAS-Net achieves significantly shorter search times (0.05-1.47 GPU Days) compared to 19 existing NAS methods, while discovering compact models (as small as 0.11M) with superior performance across five medical image benchmarks. This study offers a viable approach for model search that accommodates individualized requirements.
144.
Becktepe, Jannis; Hennig, Leona; Oeltze-Jafra, Steffen; Lindauer, Marius
Auto-nnU-Net: Towards Automated Medical Image Segmentation Technical Report
2025.
@techreport{becktepe2025autonnunetautomatedmedicalimage,
title = {Auto-nnU-Net: Towards Automated Medical Image Segmentation},
author = {Jannis Becktepe and Leona Hennig and Steffen Oeltze-Jafra and Marius Lindauer},
url = {https://arxiv.org/abs/2505.16561},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
143.
Ardila, Diego Páez; Carvalho, Thiago; Saavedra, Santiago Vasquez; Niño, Cesar Valencia; Figueiredo, Karla; Vellasco, Marley
Quantum-Inspired NAS With Attention-Based Search Spaces in Medical Applications Proceedings Article
In: 2025 IEEE Symposium on Computational Intelligence in Health and Medicine Companion (CIHM Companion), pp. 1-5, 2025.
@inproceedings{11002695,
title = {Quantum-Inspired NAS With Attention-Based Search Spaces in Medical Applications},
author = {Diego Páez Ardila and Thiago Carvalho and Santiago Vasquez Saavedra and Cesar Valencia Niño and Karla Figueiredo and Marley Vellasco},
doi = {10.1109/CIHMCompanion65205.2025.11002695},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {2025 IEEE Symposium on Computational Intelligence in Health and Medicine Companion (CIHM Companion)},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
142.
Cao, Bin; Deng, Huanyu; Hao, Yiming; Luo, Xiao
Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation Journal Article
In: Information Fusion, vol. 123, pp. 103301, 2025, ISSN: 1566-2535.
@article{CAO2025103301,
title = {Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation},
author = {Bin Cao and Huanyu Deng and Yiming Hao and Xiao Luo},
url = {https://www.sciencedirect.com/science/article/pii/S1566253525003744},
doi = {https://doi.org/10.1016/j.inffus.2025.103301},
issn = {1566-2535},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Information Fusion},
volume = {123},
pages = {103301},
abstract = {With the rapid development of artificial intelligence, medical image semantic segmentation is being used more widely. However, centralized training can lead to privacy risks. At the same time, MRI provides multiple views that together describe the anatomical structure of a lesion, but a single view may not fully capture all features. Therefore, integrating multi-view information in a federated learning setting is a key challenge for improving model generalization. This study combines federated learning, neural architecture search (NAS) and data fusion techniques to address issues related to data privacy, cross-institutional data distribution differences and multi-view information fusion in medical imaging. To achieve this, we propose the FL-MONAS framework, which leverages the advantages of NAS and federated learning. It uses a Pareto-frontier-based multi-objective optimization strategy to effectively combine 2D MRI with 3D anatomical structures, improving model performance while ensuring data privacy. Experimental results show that FL-MONAS maintains strong segmentation performance even in non-IID scenarios, providing an efficient and privacy-friendly solution for medical image analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the rapid development of artificial intelligence, medical image semantic segmentation is being used more widely. However, centralized training can lead to privacy risks. At the same time, MRI provides multiple views that together describe the anatomical structure of a lesion, but a single view may not fully capture all features. Therefore, integrating multi-view information in a federated learning setting is a key challenge for improving model generalization. This study combines federated learning, neural architecture search (NAS) and data fusion techniques to address issues related to data privacy, cross-institutional data distribution differences and multi-view information fusion in medical imaging. To achieve this, we propose the FL-MONAS framework, which leverages the advantages of NAS and federated learning. It uses a Pareto-frontier-based multi-objective optimization strategy to effectively combine 2D MRI with 3D anatomical structures, improving model performance while ensuring data privacy. Experimental results show that FL-MONAS maintains strong segmentation performance even in non-IID scenarios, providing an efficient and privacy-friendly solution for medical image analysis.
141.
Mecharbat, Lotfi Abdelkrim; Almakky, Ibrahim; Takac, Martin; Yaqub, Mohammad
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search Technical Report
2025.
@techreport{mecharbat2025mednnssupernetbasedmedicaltaskadaptiveb,
title = {MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search},
author = {Lotfi Abdelkrim Mecharbat and Ibrahim Almakky and Martin Takac and Mohammad Yaqub},
url = {https://arxiv.org/abs/2504.15865},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
140.
Wani, M. Arif; Sultan, Bisma; Ali, Sarwat; Sofi, Mukhtar Ahmad
Introduction to Deep Learning Applications Book Chapter
In: Advances in Deep Learning, Volume 2, pp. 1–14, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-3498-9.
@inbook{Wani2025c,
title = {Introduction to Deep Learning Applications},
author = {M. Arif Wani and Bisma Sultan and Sarwat Ali and Mukhtar Ahmad Sofi},
url = {https://doi.org/10.1007/978-981-96-3498-9_1},
doi = {10.1007/978-981-96-3498-9_1},
isbn = {978-981-96-3498-9},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Advances in Deep Learning, Volume 2},
pages = {1–14},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep learning has emerged as a cornerstone of modern Artificial Intelligence (AI), offering unparalleled performance across a variety of complex tasks. Its ability to automatically learn features from vast amounts of data has transformed industries ranging from computer vision to natural language processing. Among the numerous applications of deep learning, three areas have witnessed significant progress: Neural Architecture Search (NAS), steganography, and medical applications. This chapter explores application of deep learning architectures in these three areas.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Deep learning has emerged as a cornerstone of modern Artificial Intelligence (AI), offering unparalleled performance across a variety of complex tasks. Its ability to automatically learn features from vast amounts of data has transformed industries ranging from computer vision to natural language processing. Among the numerous applications of deep learning, three areas have witnessed significant progress: Neural Architecture Search (NAS), steganography, and medical applications. This chapter explores application of deep learning architectures in these three areas.
139.
Guarrasi, Valerio; Mogensen, Klara; Tassinari, Sara; Qvarlander, Sara; Soda, Paolo
Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging Technical Report
2025.
@techreport{guarrasi2025timingeverythingfindingoptimal,
title = {Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging},
author = {Valerio Guarrasi and Klara Mogensen and Sara Tassinari and Sara Qvarlander and Paolo Soda},
url = {https://arxiv.org/abs/2505.02467},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
138.
Mecharbat, Lotfi Abdelkrim; Almakky, Ibrahim; Takac, Martin; Yaqub, Mohammad
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search Technical Report
2025.
@techreport{mecharbat2025mednnssupernetbasedmedicaltaskadaptive,
title = {MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search},
author = {Lotfi Abdelkrim Mecharbat and Ibrahim Almakky and Martin Takac and Mohammad Yaqub},
url = {https://arxiv.org/abs/2504.15865},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
137.
Yu, Jiandong; Li, Tongtong; Shi, Xuerong; Zhao, Ziyang; Chen, Miao; Zhang, Yu; Wang, Junyu; Yao, Zhijun; Fang, Lei; Hu, Bin
ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification Journal Article
In: Biomedical Signal Processing and Control, vol. 104, pp. 107479, 2025, ISSN: 1746-8094.
@article{YU2025107479,
title = {ETMO-NAS: An efficient two-step multimodal one-shot NAS for lung nodules classification},
author = {Jiandong Yu and Tongtong Li and Xuerong Shi and Ziyang Zhao and Miao Chen and Yu Zhang and Junyu Wang and Zhijun Yao and Lei Fang and Bin Hu},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424015374},
doi = {https://doi.org/10.1016/j.bspc.2024.107479},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {104},
pages = {107479},
abstract = {Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Malignant lung nodules are the initial diagnostic manifestation of lung cancer. Accurate predictive classification of malignant from benign lung nodules can improve treatment efficacy and survival rate of lung cancer patients. Since current deep learning-based PET/CT pulmonary nodule-assisted diagnosis models typically rely on network architectures carefully designed by researchers, which require professional expertise and extensive prior knowledge. To combat these challenges, in this paper, we propose an efficient two-step multimodal one-shot NAS (ETMO-NAS) for searching high-performance network architectures for reliable and accurate classification of lung nodules for multimodal PET/CT data. Specifically, the step I focuses on fully training the performance of all candidate architectures in the search space using the sandwich rule and in-place distillation strategy. The step II aims to split the search space into multiple non-overlapping subsupernets by parallel operation edge decomposition strategy and then fine-tune the subsupernets further improve performance. Finally, the performance of ETMO-NAS was validated on a set of real clinical data. The experimental results show that the classification architecture searched by ETMO-NAS achieves the best performance with accuracy, precision, sensitivity, specificity, and F-1 score of 94.23%, 92.10%, 95.83%, 92.86% and 0.9388, respectively. In addition, compared with the classical CNN model and NAS model, ETMO-NAS performs better with the same inputs, but with only 1/33–1/5 of the parameters. This provides substantial evidence for the competitiveness of the model in classification tasks and presents a new approach for automated diagnosis of PET/CT pulmonary nodules. Code and models will be available at: https://github.com/yujiandong0002/ETMO-NAS.
136.
Wang, Weibo; Li, Hua
NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search Journal Article
In: Biomedical Signal Processing and Control, vol. 100, pp. 107022, 2025, ISSN: 1746-8094.
@article{WANG2025107022,
title = {NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search},
author = {Weibo Wang and Hua Li},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424010802},
doi = {https://doi.org/10.1016/j.bspc.2024.107022},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {100},
pages = {107022},
abstract = {In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.
2024
135.
AL-Sabri, Raeed; Gao, Jianliang; Chen, Jiamin; Oloulade, Babatounde Moctard; Wu, Zhenpeng; Abdullah, Monir; Hu, Xiaohua
M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions Proceedings Article
In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1783-1788, IEEE Computer Society, Los Alamitos, CA, USA, 2024.
@inproceedings{10821927,
title = { M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions },
author = {Raeed AL-Sabri and Jianliang Gao and Jiamin Chen and Babatounde Moctard Oloulade and Zhenpeng Wu and Monir Abdullah and Xiaohua Hu},
url = {https://doi.ieeecomputersociety.org/10.1109/BIBM62325.2024.10821927},
doi = {10.1109/BIBM62325.2024.10821927},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
booktitle = {2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
pages = {1783-1788},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.
134.
(Ed.)
Medical Neural Architecture Search: Survey and Taxonomy Collection
2024.
@collection{Benmeziane-ijcai24a,
title = {Medical Neural Architecture Search: Survey and Taxonomy},
author = {Hadjer Benmeziane and Imane Hamzaoui and Kaoutar El Maghraoui and Kaoutar El Maghraoui},
url = {https://www.ijcai.org/proceedings/2024/0878.pdf},
year = {2024},
date = {2024-08-03},
urldate = {2024-08-03},
booktitle = {IJCAI 2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
133.
Zhang, Jinnian; Chen, Weijie; Joshi, Tanmayee; Uyanik, Meltem; Zhang, Xiaomin; Loh, Po-Ling; Jog, Varun; Bruce, Richard; Garrett, John; McMillan, Alan
RobMedNAS: searching robust neural network architectures for medical image synthesis Journal Article
In: Biomedical Physics & Engineering Express, vol. 10, no. 5, pp. 055029, 2024.
@article{Zhang_2024,
title = {RobMedNAS: searching robust neural network architectures for medical image synthesis},
author = {Jinnian Zhang and Weijie Chen and Tanmayee Joshi and Meltem Uyanik and Xiaomin Zhang and Po-Ling Loh and Varun Jog and Richard Bruce and John Garrett and Alan McMillan},
url = {https://dx.doi.org/10.1088/2057-1976/ad6e87},
doi = {10.1088/2057-1976/ad6e87},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Biomedical Physics & Engineering Express},
volume = {10},
number = {5},
pages = {055029},
publisher = {IOP Publishing},
abstract = {Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS’s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.
132.
Asiimwe, Arnold; Das, William; Benmeziane, Hadjer; Maghraoui, Kaoutar El
EDGE2024, 2024.
@conference{Asiimwe-edge24a,
title = {EfficientMedSAM: Accelerating Medical Image Segmentation via Neural Architecture Search and Knowledge Distillation},
author = {Arnold Asiimwe and William Das and Hadjer Benmeziane and Kaoutar El Maghraoui},
url = {https://research.ibm.com/publications/efficientmedsam-accelerating-medical-image-segmentation-via-neural-architecture-search-and-knowledge-distillation},
year = {2024},
date = {2024-07-07},
urldate = {2024-07-07},
booktitle = {EDGE2024},
journal = {EDGE 2024},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
131.
Berezsky, O. M.; Liashchynskyi, P. B.
METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS Journal Article
In: Radio Electronics, Computer Science, Control, no. 1, pp. 104, 2024.
@article{Berezsky_Liashchynskyi_2024,
title = {METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS},
author = {O. M. Berezsky and P. B. Liashchynskyi},
url = {http://ric.zntu.edu.ua/article/view/300976},
doi = {10.15588/1607-3274-2024-1-10},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Radio Electronics, Computer Science, Control},
number = {1},
pages = {104},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
130.
Rajesh, Chilukamari; Sadam, Ravichandra; Kumar, Sushil
Automated Deep Learning Models for Medical Image Segmentation and Denoising Proceedings Article
In: 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), pp. 1-7, 2024.
@inproceedings{10815837,
title = {Automated Deep Learning Models for Medical Image Segmentation and Denoising},
author = {Chilukamari Rajesh and Ravichandra Sadam and Sushil Kumar},
url = {https://ieeexplore.ieee.org/abstract/document/10815837},
doi = {10.1109/ICSPCS63175.2024.10815837},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 17th International Conference on Signal Processing and Communication System (ICSPCS)},
pages = {1-7},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
129.
Franco-Gaona, Erick; Avila-Garcia, Maria-Susana; Cruz-Aceves, Ivan; Orocio-Garcia, Hiram-Efrain; Escobedo-Gordillo, Andres; Brieva, Jorge
Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis Proceedings Article
In: 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), pp. 1-4, 2024.
@inproceedings{10783513,
title = {Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis},
author = {Erick Franco-Gaona and Maria-Susana Avila-Garcia and Ivan Cruz-Aceves and Hiram-Efrain Orocio-Garcia and Andres Escobedo-Gordillo and Jorge Brieva},
url = {https://ieeexplore.ieee.org/abstract/document/10783513},
doi = {10.1109/SIPAIM62974.2024.10783513},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)},
pages = {1-4},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
128.
Kuş, Zeki; Kiraz, Berna; Aydin, Musa; Kiraz, Alper
BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification Proceedings Article
In: Garcia, Fausto P.; Jamil, Akhtar; Hameed, Alaa Ali; Ortis, Alessandro; Ramirez, Isaac Segovia (Ed.): Recent Trends and Advances in Artificial Intelligence, pp. 539–550, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-70924-1.
@inproceedings{10.1007/978-3-031-70924-1_41,
title = {BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification},
author = {Zeki Kuş and Berna Kiraz and Musa Aydin and Alper Kiraz},
editor = {Fausto P. Garcia and Akhtar Jamil and Alaa Ali Hameed and Alessandro Ortis and Isaac Segovia Ramirez},
isbn = {978-3-031-70924-1},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Recent Trends and Advances in Artificial Intelligence},
pages = {539–550},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.
127.
Ji, Yuanfeng; 纪源丰,
Towards efficient deep learning for medical image analysis Bachelor Thesis
2024.
@bachelorthesis{HKUHUB_10722_350322,
title = {Towards efficient deep learning for medical image analysis},
author = {Yuanfeng Ji and 纪源丰},
url = {http://hdl.handle.net/10722/350322},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {HKU Theses Online (HKUTO)},
abstract = {The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.
126.
Chen, Xi; Lv, Jiahuan; Wang, Zeyu; Qin, Genggeng; Zhou, Zhiguo
In: Computers in Biology and Medicine, vol. 183, pp. 109299, 2024, ISSN: 0010-4825.
@article{CHEN2024109299,
title = {Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis},
author = {Xi Chen and Jiahuan Lv and Zeyu Wang and Genggeng Qin and Zhiguo Zhou},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524013842},
doi = {https://doi.org/10.1016/j.compbiomed.2024.109299},
issn = {0010-4825},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers in Biology and Medicine},
volume = {183},
pages = {109299},
abstract = {Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.
125.
Park, Eunbin; Lee, Youngjoo
mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-11, 2024.
@article{10720208,
title = {mDARTS: Searching ML-Based ECG Classifiers against Membership Inference Attacks},
author = {Eunbin Park and Youngjoo Lee},
url = {https://ieeexplore.ieee.org/abstract/document/10720208},
doi = {10.1109/JBHI.2024.3481505},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
124.
Zhang, Qing; Shao, Dan; Lin, Lin; Gong, Guoliang; Xu, Rui; Kido, Shoji; Cui, HongWei
Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-12, 2024.
@article{10716760,
title = {Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS},
author = {Qing Zhang and Dan Shao and Lin Lin and Guoliang Gong and Rui Xu and Shoji Kido and HongWei Cui},
url = {https://ieeexplore.ieee.org/document/10716760},
doi = {10.1109/JBHI.2024.3481012},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
123.
Wu, Yu; Fan, Hailong; Ying, Weiqin; Zhou, Zekun; Zheng, Qiaoqiao; Zhang, Jiajian
Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation Proceedings Article
In: Tan, Ying; Shi, Yuhui (Ed.): Advances in Swarm Intelligence, pp. 112–123, Springer Nature Singapore, Singapore, 2024, ISBN: 978-981-97-7184-4.
@inproceedings{10.1007/978-981-97-7184-4_10,
title = {Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation},
author = {Yu Wu and Hailong Fan and Weiqin Ying and Zekun Zhou and Qiaoqiao Zheng and Jiajian Zhang},
editor = {Ying Tan and Yuhui Shi},
url = {https://link.springer.com/chapter/10.1007/978-981-97-7184-4_10},
isbn = {978-981-97-7184-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Advances in Swarm Intelligence},
pages = {112–123},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a U-Net++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a U-Net++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.
122.
Domanski, Peter; Ray, Aritra; Lafata, Kyle; Firouzi, Farshad; Chakrabarty, Krishnendu; Pflüger, Dirk
Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics Journal Article
In: Biocybernetics and Biomedical Engineering, vol. 44, no. 3, pp. 481-500, 2024, ISSN: 0208-5216.
@article{DOMANSKI2024481,
title = {Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics},
author = {Peter Domanski and Aritra Ray and Kyle Lafata and Farshad Firouzi and Krishnendu Chakrabarty and Dirk Pflüger},
url = {https://www.sciencedirect.com/science/article/pii/S0208521624000536},
doi = {https://doi.org/10.1016/j.bbe.2024.07.006},
issn = {0208-5216},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biocybernetics and Biomedical Engineering},
volume = {44},
number = {3},
pages = {481-500},
abstract = {For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.
121.
Chaiyarin, Sinee; Rojbundit, Napassorn; Piyabenjarad, Panichanok; Limpitigranon, Pimpattra; Wisitthipakdeekul, Siraprapa; Nonthasaen, Pawaree; Achararit, Paniti
Neural architecture search for medicine: A survey Journal Article
In: Informatics in Medicine Unlocked, vol. 50, pp. 101565, 2024, ISSN: 2352-9148.
@article{CHAIYARIN2024101565,
title = {Neural architecture search for medicine: A survey},
author = {Sinee Chaiyarin and Napassorn Rojbundit and Panichanok Piyabenjarad and Pimpattra Limpitigranon and Siraprapa Wisitthipakdeekul and Pawaree Nonthasaen and Paniti Achararit},
url = {https://www.sciencedirect.com/science/article/pii/S2352914824001217},
doi = {https://doi.org/10.1016/j.imu.2024.101565},
issn = {2352-9148},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Informatics in Medicine Unlocked},
volume = {50},
pages = {101565},
abstract = {In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.
120.
Sheng, Yi; Yang, Junhuan; Li, Jinyang; Alaina, James; Xu, Xiaowei; Shi, Yiyu; Hu, Jingtong; Jiang, Weiwen; Yang, Lei
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset Proceedings Article
In: Linguraru, Marius George; Dou, Qi; Feragen, Aasa; Giannarou, Stamatia; Glocker, Ben; Lekadir, Karim; Schnabel, Julia A. (Ed.): Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 - 27th International Conference, Marrakesh, Morocco, October 6-10, 2024, Proceedings, Part X, pp. 153–163, Springer, 2024.
@inproceedings{DBLP:conf/miccai/ShengYLAXSHJY24,
title = {Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset},
author = {Yi Sheng and Junhuan Yang and Jinyang Li and James Alaina and Xiaowei Xu and Yiyu Shi and Jingtong Hu and Weiwen Jiang and Lei Yang},
editor = {Marius George Linguraru and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Julia A. Schnabel},
url = {https://doi.org/10.1007/978-3-031-72117-5_15},
doi = {10.1007/978-3-031-72117-5_15},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI
2024 - 27th International Conference, Marrakesh, Morocco, October
6-10, 2024, Proceedings, Part X},
volume = {15010},
pages = {153–163},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
119.
Wei, Jiahong; Xue, Bing; Zhang, Mengjie
EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation Proceedings Article
In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 422–430, Association for Computing Machinery, Melbourne, VIC, Australia, 2024, ISBN: 9798400704949.
@inproceedings{10.1145/3638529.3654041,
title = {EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation},
author = {Jiahong Wei and Bing Xue and Mengjie Zhang},
url = {https://doi.org/10.1145/3638529.3654041},
doi = {10.1145/3638529.3654041},
isbn = {9798400704949},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {422–430},
publisher = {Association for Computing Machinery},
address = {Melbourne, VIC, Australia},
series = {GECCO '24},
abstract = {Recently, deep learning-based methods have become the mainstream for medical image segmentation. Since manually designing deep neural networks (DNNs) is laborious and time-consuming, neural architecture search (NAS) becomes a popular stream for automatically designing DNNs for medical image segmentation. However, existing NAS work for medical image segmentation is still computationally expensive. Given the limited computation power, it is not always applicable to search for a well-performing model from an enlarged search space. In this paper, we propose EZUAS, a novel method of evolutionary zero-shot NAS for medical image segmentation, to address these issues. First, a new search space is designed for the automated design of DNNs. A genetic algorithm (GA) with an aligned crossover operation is then leveraged to search the network architectures under the model complexity constraints to get performant and lightweight models. In addition, a variable-length integer encoding scheme is devised to encode the candidate U-shaped DNNs with different stages. We conduct experiments on two commonly used medical image segmentation datasets to verify the effectiveness of the proposed EZUAS. Compared with the state-of-the-art methods, the proposed method can find a model much faster (about 0.04 GPU day) and achieve the best performance with lower computational complexity.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, deep learning-based methods have become the mainstream for medical image segmentation. Since manually designing deep neural networks (DNNs) is laborious and time-consuming, neural architecture search (NAS) becomes a popular stream for automatically designing DNNs for medical image segmentation. However, existing NAS work for medical image segmentation is still computationally expensive. Given the limited computation power, it is not always applicable to search for a well-performing model from an enlarged search space. In this paper, we propose EZUAS, a novel method of evolutionary zero-shot NAS for medical image segmentation, to address these issues. First, a new search space is designed for the automated design of DNNs. A genetic algorithm (GA) with an aligned crossover operation is then leveraged to search the network architectures under the model complexity constraints to get performant and lightweight models. In addition, a variable-length integer encoding scheme is devised to encode the candidate U-shaped DNNs with different stages. We conduct experiments on two commonly used medical image segmentation datasets to verify the effectiveness of the proposed EZUAS. Compared with the state-of-the-art methods, the proposed method can find a model much faster (about 0.04 GPU day) and achieve the best performance with lower computational complexity.
118.
Ali, Muhammad Junaid; Moalic, Laurent; Essaid, Mokhtar; Idoumghar, Lhassane
Evolutionary Neural Architecture Search for 2D and 3D Medical Image Classification Proceedings Article
In: Franco, Leonardo; Mulatier, Clélia; Paszynski, Maciej; Krzhizhanovskaya, Valeria V.; Dongarra, Jack J.; Sloot, Peter M. A. (Ed.): Computational Science – ICCS 2024, pp. 131–146, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-63751-3.
@inproceedings{10.1007/978-3-031-63751-3_9,
title = {Evolutionary Neural Architecture Search for 2D and 3D Medical Image Classification},
author = {Muhammad Junaid Ali and Laurent Moalic and Mokhtar Essaid and Lhassane Idoumghar},
editor = {Leonardo Franco and Clélia Mulatier and Maciej Paszynski and Valeria V. Krzhizhanovskaya and Jack J. Dongarra and Peter M. A. Sloot},
url = {https://link.springer.com/chapter/10.1007/978-3-031-63751-3_9},
isbn = {978-3-031-63751-3},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Computational Science – ICCS 2024},
pages = {131–146},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Designing deep learning architectures is a challenging and time-consuming task. To address this problem, Neural Architecture Search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://github.com/Junaid199f/evo_nas_med_2d_3d.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Designing deep learning architectures is a challenging and time-consuming task. To address this problem, Neural Architecture Search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://github.com/Junaid199f/evo_nas_med_2d_3d.
117.
Cardoso, Fabio; Vellasco, Marley; Figueiredo, Karla
Comparative Study Between Q-NAS and Traditional CNNs for Brain Tumor Classification Proceedings Article
In: Iliadis, Lazaros; Maglogiannis, Ilias; Papaleonidas, Antonios; Pimenidis, Elias; Jayne, Chrisina (Ed.): Engineering Applications of Neural Networks, pp. 93–105, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-62495-7.
@inproceedings{10.1007/978-3-031-62495-7_8,
title = {Comparative Study Between Q-NAS and Traditional CNNs for Brain Tumor Classification},
author = {Fabio Cardoso and Marley Vellasco and Karla Figueiredo},
editor = {Lazaros Iliadis and Ilias Maglogiannis and Antonios Papaleonidas and Elias Pimenidis and Chrisina Jayne},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62495-7_8},
isbn = {978-3-031-62495-7},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Engineering Applications of Neural Networks},
pages = {93–105},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Brain tumours caused approximately 251,329 deaths worldwide in 2020, with the primary diagnostic method for these tumours involving medical imaging. In recent years, many works and applications have observed the use of Artificial Intelligence-based models using Convolution Neural Networks (CNNs) to identify health problems using images. In our study, we searched for new architectures based on CNN using the Q-NAS algorithm. We compared its performance and number of parameters with traditional architectures such as VGG, ResNet, and MobileNet to classify types of brain tumors in MRI images. The best architecture found by Q-NAS achieved an accuracy of 92% on the test data set, with a model with less than one million parameters, which is much smaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Brain tumours caused approximately 251,329 deaths worldwide in 2020, with the primary diagnostic method for these tumours involving medical imaging. In recent years, many works and applications have observed the use of Artificial Intelligence-based models using Convolution Neural Networks (CNNs) to identify health problems using images. In our study, we searched for new architectures based on CNN using the Q-NAS algorithm. We compared its performance and number of parameters with traditional architectures such as VGG, ResNet, and MobileNet to classify types of brain tumors in MRI images. The best architecture found by Q-NAS achieved an accuracy of 92% on the test data set, with a model with less than one million parameters, which is much smaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.
116.
Kaur, Tanvir; Kamboj, Shivani; Singh, Lovedeep; Tamanna,
Advanced YOLO-NAS-Based Detection and Screening of Brain Tumors Using Medical Diagnostic Proceedings Article
In: 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), pp. 1-6, 2024.
@inproceedings{10531625,
title = {Advanced YOLO-NAS-Based Detection and Screening of Brain Tumors Using Medical Diagnostic},
author = {Tanvir Kaur and Shivani Kamboj and Lovedeep Singh and Tamanna},
url = {https://ieeexplore.ieee.org/abstract/document/10531625},
doi = {10.1109/AIMLA59606.2024.10531625},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
115.
Saeedizadeh, Narges; Jalali, Seyed Mohammad Jafar; Khan, Burhan; Kebria, Parham Mohsenzadeh; Mohamed, Shady
A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation Journal Article
In: Knowledge-Based Systems, vol. 296, pp. 111966, 2024, ISSN: 0950-7051.
@article{SAEEDIZADEH2024111966,
title = {A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation},
author = {Narges Saeedizadeh and Seyed Mohammad Jafar Jalali and Burhan Khan and Parham Mohsenzadeh Kebria and Shady Mohamed},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124006002},
doi = {https://doi.org/10.1016/j.knosys.2024.111966},
issn = {0950-7051},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Knowledge-Based Systems},
volume = {296},
pages = {111966},
abstract = {Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined with dice loss is commonly used in many segmentation tasks, road extraction brings up a unique challenge due to class imbalance. To address this, we introduce a combination of loss function. This function merges cross-entropy with weighted Dice loss, focusing on elevating the importance of the road class by assigning it a weight (⍵), while background Dice values are disregarded. The results indicate that the optimal weight for the proposed model equals 2. Additionally, our work challenges the assumption that increased model parameters or depth inherently leads to improved performance. Therefore, we establish search spaces 2,3,4,5,6,7 and 8 to automatically choose the optimal depth for model. We present promising segmentation results for our proposed method, achieved without any pretraining on the Massachusetts road dataset. Furthermore, these results are compared with those of 14 models categorized into four groups: U-Net, Segnet, FCN8, and Nas-U-Net.},
keywords = {},
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tppubtype = {article}
}
Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined with dice loss is commonly used in many segmentation tasks, road extraction brings up a unique challenge due to class imbalance. To address this, we introduce a combination of loss function. This function merges cross-entropy with weighted Dice loss, focusing on elevating the importance of the road class by assigning it a weight (⍵), while background Dice values are disregarded. The results indicate that the optimal weight for the proposed model equals 2. Additionally, our work challenges the assumption that increased model parameters or depth inherently leads to improved performance. Therefore, we establish search spaces 2,3,4,5,6,7 and 8 to automatically choose the optimal depth for model. We present promising segmentation results for our proposed method, achieved without any pretraining on the Massachusetts road dataset. Furthermore, these results are compared with those of 14 models categorized into four groups: U-Net, Segnet, FCN8, and Nas-U-Net.
114.
Xie, Lunchen; Lomurno, Eugenio; Gambella, Matteo; Ardagna, Danilo; Roveri, Manuel; Matteucci, Matteo; Shi, Qingjiang
A Lightweight Neural Architecture Search Model for Medical Image Classification Technical Report
2024.
@techreport{xie2024lightweight,
title = {A Lightweight Neural Architecture Search Model for Medical Image Classification},
author = {Lunchen Xie and Eugenio Lomurno and Matteo Gambella and Danilo Ardagna and Manuel Roveri and Matteo Matteucci and Qingjiang Shi},
url = {https://arxiv.org/abs/2405.03462},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
113.
Berezsky, Oleh; Liashchynskyi, Petro; Pitsun, Oleh; Izonin, Ivan
Synthesis of Convolutional Neural Network architectures for biomedical image classification Journal Article
In: Biomedical Signal Processing and Control, vol. 95, pp. 106325, 2024, ISSN: 1746-8094.
@article{BEREZSKY2024106325,
title = {Synthesis of Convolutional Neural Network architectures for biomedical image classification},
author = {Oleh Berezsky and Petro Liashchynskyi and Oleh Pitsun and Ivan Izonin},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424003835},
doi = {https://doi.org/10.1016/j.bspc.2024.106325},
issn = {1746-8094},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {95},
pages = {106325},
abstract = {Convolutional Neural Networks (CNNs) are frequently used for image classification. This is crucial for the biomedical image classification used for automatic diagnosis in oncology. Designing optimal convolutional neural network architectures is a routine procedure that requires expert knowledge of computer vision and biomedical image features. To address this issue, we developed an automatic method for finding optimal CNN architectures. Our two-step method includes a genetic algorithm-based micro- and macro-search. Micro-search aims to find the optimal cell architecture based on the number of nodes and a set of predefined operations between nodes. Macro-search identifies the optimal number of cells and the operations between them to obtain the final optimal architecture. We obtained several optimal CNN architectures using the developed method of automatic architecture search. We conducted several computer experiments using cytological image classification as an example. The studies’ findings demonstrated that cytological image classification accuracy is higher compared to the classification accuracy of known CNN architectures (VGG-16, AlexNet, LeNet-5, ResNet-18, ResNet-50, MobileNetV3). The method is efficient because the search time for optimal architectures is short. Additionally, the method of optimal architecture search can be used for the synthesis of architectures used for the classification of other classes of biomedical images.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Convolutional Neural Networks (CNNs) are frequently used for image classification. This is crucial for the biomedical image classification used for automatic diagnosis in oncology. Designing optimal convolutional neural network architectures is a routine procedure that requires expert knowledge of computer vision and biomedical image features. To address this issue, we developed an automatic method for finding optimal CNN architectures. Our two-step method includes a genetic algorithm-based micro- and macro-search. Micro-search aims to find the optimal cell architecture based on the number of nodes and a set of predefined operations between nodes. Macro-search identifies the optimal number of cells and the operations between them to obtain the final optimal architecture. We obtained several optimal CNN architectures using the developed method of automatic architecture search. We conducted several computer experiments using cytological image classification as an example. The studies’ findings demonstrated that cytological image classification accuracy is higher compared to the classification accuracy of known CNN architectures (VGG-16, AlexNet, LeNet-5, ResNet-18, ResNet-50, MobileNetV3). The method is efficient because the search time for optimal architectures is short. Additionally, the method of optimal architecture search can be used for the synthesis of architectures used for the classification of other classes of biomedical images.
112.
Ali, Muhammad Junaid; Moalic, Laurent; Essaid, Mokhtar; Idoumghar, Lhassane
Robust Neural Architecture Search Using Differential Evolution for Medical Images Proceedings Article
In: Smith, Stephen; Correia, João; Cintrano, Christian (Ed.): Applications of Evolutionary Computation, pp. 163–179, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-56855-8.
@inproceedings{10.1007/978-3-031-56855-8_10,
title = {Robust Neural Architecture Search Using Differential Evolution for Medical Images},
author = {Muhammad Junaid Ali and Laurent Moalic and Mokhtar Essaid and Lhassane Idoumghar},
editor = {Stephen Smith and João Correia and Christian Cintrano},
url = {https://link.springer.com/chapter/10.1007/978-3-031-56855-8_10},
isbn = {978-3-031-56855-8},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Applications of Evolutionary Computation},
pages = {163–179},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Recent studies have demonstrated that Convolutional Neural Network (CNN) architectures are sensitive to adversarial attacks with imperceptible permutations. Adversarial attacks on medical images may cause manipulated decisions and decrease the performance of the diagnosis system. The robustness of medical systems is crucial, as it assures an improved healthcare system and assists medical professionals in making decisions. Various studies have been proposed to secure medical systems against adversarial attacks, but they have used handcrafted architectures. This study proposes an evolutionary Neural Architecture Search (NAS) approach for searching robust architectures for medical image classification. The Differential Evolution (DE) algorithm is used as a search algorithm. Furthermore, we utilize an attention-based search space consisting of five different attention layers and sixteen convolution and pooling operations. Experiments on multiple MedMNIST datasets show that the proposed approach has achieved better results than deep learning architectures and a robust NAS approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent studies have demonstrated that Convolutional Neural Network (CNN) architectures are sensitive to adversarial attacks with imperceptible permutations. Adversarial attacks on medical images may cause manipulated decisions and decrease the performance of the diagnosis system. The robustness of medical systems is crucial, as it assures an improved healthcare system and assists medical professionals in making decisions. Various studies have been proposed to secure medical systems against adversarial attacks, but they have used handcrafted architectures. This study proposes an evolutionary Neural Architecture Search (NAS) approach for searching robust architectures for medical image classification. The Differential Evolution (DE) algorithm is used as a search algorithm. Furthermore, we utilize an attention-based search space consisting of five different attention layers and sixteen convolution and pooling operations. Experiments on multiple MedMNIST datasets show that the proposed approach has achieved better results than deep learning architectures and a robust NAS approach.
111.
Liu, Xin; Tian, Jie; Duan, Peiyong; Yu, Qian; Wang, Gaige; Wang, Yingjie
GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis Journal Article
In: Computers in Biology and Medicine, vol. 171, pp. 108118, 2024, ISSN: 0010-4825.
@article{LIU2024108118,
title = {GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis},
author = {Xin Liu and Jie Tian and Peiyong Duan and Qian Yu and Gaige Wang and Yingjie Wang},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524002026},
doi = {https://doi.org/10.1016/j.compbiomed.2024.108118},
issn = {0010-4825},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers in Biology and Medicine},
volume = {171},
pages = {108118},
abstract = {Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.
110.
Cui, Suhan; Wang, Jiaqi; Zhong, Yuan; Liu, Han; Wang, Ting; Ma, Fenglong
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions Technical Report
2024.
@techreport{cui2024automated,
title = {Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions},
author = {Suhan Cui and Jiaqi Wang and Yuan Zhong and Han Liu and Ting Wang and Fenglong Ma},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
109.
Fuentes-Tomás, José Antonio; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Jiménez, Rodolfo Hernandez
Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images Proceedings Article
In: Calvo, Hiram; Martínez-Villaseñor, Lourdes; Ponce, Hiram; Cabada, Ramón Zatarain; Rivera, Martín Montes; Mezura-Montes, Efrén (Ed.): Advances in Computational Intelligence. MICAI 2023 International Workshops, pp. 397–408, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-51940-6.
@inproceedings{10.1007/978-3-031-51940-6_30,
title = {Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images},
author = {José Antonio Fuentes-Tomás and Héctor Gabriel Acosta-Mesa and Efrén Mezura-Montes and Rodolfo Hernandez Jiménez},
editor = {Hiram Calvo and Lourdes Martínez-Villaseñor and Hiram Ponce and Ramón Zatarain Cabada and Martín Montes Rivera and Efrén Mezura-Montes},
url = {https://link.springer.com/chapter/10.1007/978-3-031-51940-6_30#citeas},
isbn = {978-3-031-51940-6},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Advances in Computational Intelligence. MICAI 2023 International Workshops},
pages = {397–408},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are characterized by their noise. A segmentation system is required to recognize placenta features. U-Net architecture is a convolutional neural network that has become popular in the literature for medical image segmentation tasks. However, this type is a general-purpose network that requires great expertise to design and may only be applicable in some domains. The evolutionary computation overcomes this limitation, leading to the automatic design of convolutional neural networks. This work proposes a U-Net-based neural architecture search algorithm to construct convolutional neural networks applied in the placenta segmentation on 2D ultrasound images. The results show that the proposed algorithm allows a decrease in the number of parameters of U-Net, ranging from 80 to 98%. Moreover, the segmentation performance achieves a competitive level compared to U-Net, with a difference of 0.012 units in the Dice index.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are characterized by their noise. A segmentation system is required to recognize placenta features. U-Net architecture is a convolutional neural network that has become popular in the literature for medical image segmentation tasks. However, this type is a general-purpose network that requires great expertise to design and may only be applicable in some domains. The evolutionary computation overcomes this limitation, leading to the automatic design of convolutional neural networks. This work proposes a U-Net-based neural architecture search algorithm to construct convolutional neural networks applied in the placenta segmentation on 2D ultrasound images. The results show that the proposed algorithm allows a decrease in the number of parameters of U-Net, ranging from 80 to 98%. Moreover, the segmentation performance achieves a competitive level compared to U-Net, with a difference of 0.012 units in the Dice index.
108.
Liu, Jianwei Zhao Jie Li Xin
Evolutionary Neural Architecture Search and Its Applications in Healthcare Journal Article
In: Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 143–185, 2024, ISSN: 1526-1506.
@article{cmes.2023.030391,
title = {Evolutionary Neural Architecture Search and Its Applications in Healthcare},
author = {Jianwei Zhao Jie Li Xin Liu},
url = {http://www.techscience.com/CMES/v139n1/55101},
doi = {10.32604/cmes.2023.030391},
issn = {1526-1506},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computer Modeling in Engineering & Sciences},
volume = {139},
number = {1},
pages = {143–185},
abstract = {Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.
107.
Yang, Dong; Roth, Holger R.; Wang, Xiaosong; Xu, Ziyue; Xu, Daguang
In: Zhou, S. Kevin; Greenspan, Hayit; Shen, Dinggang (Ed.): Deep Learning for Medical Image Analysis (Second Edition), pp. 281-298, Academic Press, 2024, ISBN: 978-0-323-85124-4.
@incollection{YANG2024281,
title = {Chapter 10 - Dynamic inference using neural architecture search in medical image segmentation: From a novel adaptation perspective},
author = {Dong Yang and Holger R. Roth and Xiaosong Wang and Ziyue Xu and Daguang Xu},
editor = {S. Kevin Zhou and Hayit Greenspan and Dinggang Shen},
url = {https://www.sciencedirect.com/science/article/pii/B9780323851244000210},
doi = {https://doi.org/10.1016/B978-0-32-385124-4.00021-0},
isbn = {978-0-323-85124-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Deep Learning for Medical Image Analysis (Second Edition)},
pages = {281-298},
publisher = {Academic Press},
edition = {Second Edition},
series = {The MICCAI Society book Series},
abstract = {Data inconsistency in medical imaging acquisition has been existing for decades, which creates difficulties when researchers adopt learning-based processing methods to unknown data. This issue is mostly caused by medical image scanners from different vendors, inconsistent scanning protocols, anatomy discrepancy among populations, environmental artifacts or other related factors. For instance, large appearance variance may exist in 3D T2-weighted brain MRI from different institutions or hospitals, even scanned with the same scanning protocols. Meanwhile, the data inconsistency downgrades the performance of machine learning models for medical image processing, such as organ or tumor segmentation, when models face unknown data at inference with pre-trained models. To alleviate the potential side effects caused by the data inconsistency, we propose a novel approach to improve model generalizability and transferability for unknown data leveraging the concepts from neural architecture search. We build a general “super-net” enabling multiple candidate modules in parallel to represent multi-scale contextual features at different network levels, respectively. After the training of the super-net is accomplished, a unique and optimal architecture for each data point is determined with guidance of additional model constraints at inference. We also propose a novel path sampling strategy to enable “fair” model training. Our experiments show that the proposed approach has clear advantages over the conventional neural network deployment in terms of segmentation performance and generalization in the unknown images.},
keywords = {},
pubstate = {published},
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}
Data inconsistency in medical imaging acquisition has been existing for decades, which creates difficulties when researchers adopt learning-based processing methods to unknown data. This issue is mostly caused by medical image scanners from different vendors, inconsistent scanning protocols, anatomy discrepancy among populations, environmental artifacts or other related factors. For instance, large appearance variance may exist in 3D T2-weighted brain MRI from different institutions or hospitals, even scanned with the same scanning protocols. Meanwhile, the data inconsistency downgrades the performance of machine learning models for medical image processing, such as organ or tumor segmentation, when models face unknown data at inference with pre-trained models. To alleviate the potential side effects caused by the data inconsistency, we propose a novel approach to improve model generalizability and transferability for unknown data leveraging the concepts from neural architecture search. We build a general “super-net” enabling multiple candidate modules in parallel to represent multi-scale contextual features at different network levels, respectively. After the training of the super-net is accomplished, a unique and optimal architecture for each data point is determined with guidance of additional model constraints at inference. We also propose a novel path sampling strategy to enable “fair” model training. Our experiments show that the proposed approach has clear advantages over the conventional neural network deployment in terms of segmentation performance and generalization in the unknown images.
106.
Fuentes-Tomás, José-Antonio; Mezura-Montes, Efrén; Acosta-Mesa, Héctor-Gabriel; Márquez-Grajales, Aldo
Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10391062,
title = {Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation},
author = {José-Antonio Fuentes-Tomás and Efrén Mezura-Montes and Héctor-Gabriel Acosta-Mesa and Aldo Márquez-Grajales},
url = {https://ieeexplore.ieee.org/abstract/document/10391062},
doi = {10.1109/TEVC.2024.3353182},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
105.
Wang, Yan; Zhen, Liangli; Zhang, Jianwei; Li, Miqing; Zhang, Lei; Wang, Zizhou; Feng, Yangqin; Xue, Yu; Wang, Xiao; Chen, Zheng; Luo, Tao; Goh, Rich Siow Mong; Liu, Yong
MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10391077,
title = {MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis},
author = {Yan Wang and Liangli Zhen and Jianwei Zhang and Miqing Li and Lei Zhang and Zizhou Wang and Yangqin Feng and Yu Xue and Xiao Wang and Zheng Chen and Tao Luo and Rich Siow Mong Goh and Yong Liu},
url = {https://ieeexplore.ieee.org/abstract/document/10391077/authors#authors},
doi = {10.1109/TEVC.2024.3352641},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
104.
Şahin, Emrullah; Özdemir, Durmuş; Temurtaş, Hasan
Multi-objective optimization of ViT architecture for efficient brain tumor classification Journal Article
In: Biomedical Signal Processing and Control, vol. 91, pp. 105938, 2024, ISSN: 1746-8094.
@article{SAHIN2024105938,
title = {Multi-objective optimization of ViT architecture for efficient brain tumor classification},
author = {Emrullah Şahin and Durmuş Özdemir and Hasan Temurtaş},
url = {https://www.sciencedirect.com/science/article/pii/S174680942301371X},
doi = {https://doi.org/10.1016/j.bspc.2023.105938},
issn = {1746-8094},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {91},
pages = {105938},
abstract = {This study presents an advanced approach to optimizing the Vision Transformer (ViT) network for brain tumor classification in 2D MRI images, utilizing Bayesian Multi-Objective (BMO) optimization techniques. Rather than merely addressing the limitations of the standard ViT model, our objective was to enhance its overall efficiency and effectiveness. The application of BMO enabled us to fine-tune the architectural parameters of the ViT network, resulting in a model that was not only twice as fast but also four times smaller in size compared to the original. In terms of performance, the optimized ViT model achieved notable improvements, with a 1.48 % increase in validation accuracy, a 3.23 % rise in the F1-score, and a 3.36 % improvement in precision. These substantial enhancements highlight the potential of integrating BMO with visual transformer-based models, suggesting a promising direction for future research in achieving high efficiency and accuracy in complex classification tasks.},
keywords = {},
pubstate = {published},
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}
This study presents an advanced approach to optimizing the Vision Transformer (ViT) network for brain tumor classification in 2D MRI images, utilizing Bayesian Multi-Objective (BMO) optimization techniques. Rather than merely addressing the limitations of the standard ViT model, our objective was to enhance its overall efficiency and effectiveness. The application of BMO enabled us to fine-tune the architectural parameters of the ViT network, resulting in a model that was not only twice as fast but also four times smaller in size compared to the original. In terms of performance, the optimized ViT model achieved notable improvements, with a 1.48 % increase in validation accuracy, a 3.23 % rise in the F1-score, and a 3.36 % improvement in precision. These substantial enhancements highlight the potential of integrating BMO with visual transformer-based models, suggesting a promising direction for future research in achieving high efficiency and accuracy in complex classification tasks.
103.
Gao, Jianliang; Wu, Zhenpeng; Al-Sabri, Raeed; Oloulade, Babatounde Moctard; Chen, Jiamin
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