Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification

He, Xin; Ying, Guohao; Zhang, Jiyong; Chu, Xiaowen

doi:10.1007/978-3-031-16431-6_53

Cited by 4 publications

(5 citation statements)

References 33 publications

(42 reference statements)

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“…For thorax disease and COVID-19, Louati et al [145,160] used GA to compress the deep CNN. He et al [155] proposed efficient evolutionary multiobjective architecture search (EMARS) to implement COVID-19 computed tomography (CT) classification. The advanced multiobjective evolutionary NAS can not only improve model performance but also reduce network complexity in healthcare.…”

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

“…Secondly, evolutionary NAS can adapt to meet the multiple requirements of a given task. Multiobjective evolutionary NAS applications in healthcare can attain high accuracy, improve model performance and reduce model size [2,71,149,[153][154][155]. Large-scale multiobjective neuroevolution strategies [73,[110][111][112][113] and distributed parallel EAs strategies [80,91,121,137,138] can accelerate the evolution process and search process of NAS.…”

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

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Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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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.

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Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

Section: Nas For Classification Of Disease Diagnosismentioning

confidence: 99%

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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show abstract

“…It is not surprising that NAS methodologies have been employed for COVID-19 image analysis applications (51-53) and promising results have been reported. For instance, He et al (52) presented an efficient evolutionary multiobjective neural architecture search (EMARS) framework for the purpose of automatically searching for 3D neural architectures for COVID-19…”

Section: Optimal and Efficient Deep Learningmentioning

confidence: 99%

“…chest CT scan classification. Through the EMARS framework, the authors used a weight sharing strategy to significantly improve search efficiency and achieve a lightweight model (3.39 MB) that outperformed three baseline human-designed models, that is, ResNet3D101 (325.21 MB), DenseNet3D121 (43.06 MB), and MC3 18 (43.84 MB) (52). In addition, the search space of the EMARS framework enabled the class activation mapping algorithm to be easily embedded into all searched models, providing better interpretability for imaging-based diagnosis by visualization of the affected areas.…”

Section: Neural Architecture Searchmentioning

confidence: 99%

Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

Liu

Siegel

Shen

2022

Annu. Rev. Biomed. Eng.

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The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in diagnosis, prediction, and management for COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Neural architecture search (NAS) (Elsken, Metzen, and Hutter 2019;He, Zhao, and Chu 2021) has been widely used to discover models automatically in various tasks Ying et al 2022;He et al 2022). Vanilla NAS (Zoph and Le 2017;Real et al 2019) trains and evaluates each architecture separately, which obtains the true performance of all searched architectures at the cost of substantial computations.…”

Section: Introductionmentioning

confidence: 99%

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

Yao

Wang

et al. 2023

AAAI

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One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.e., subnet). However, the inconsistency of characteristics among subnets incurs serious interference in the optimization, resulting in poor performance ranking correlation of subnets. Subsequent explorations decompose supernet weights via a particular criterion, e.g., gradient matching, to reduce the interference; yet they suffer from huge computational cost and low space separability. In this work, we propose a lightweight and effective local intrinsic dimension (LID)-based method NAS-LID. NAS-LID evaluates the geometrical properties of architectures by calculating the low-cost LID features layer-by-layer, and the similarity characterized by LID enjoys better separability compared with gradients, which thus effectively reduces the interference among subnets. Extensive experiments on NASBench-201 indicate that NAS-LID achieves superior performance with better efficiency. Specifically, compared to the gradient-driven method, NAS-LID can save up to 86% of GPU memory overhead when searching on NASBench-201. We also demonstrate the effectiveness of NAS-LID on ProxylessNAS and OFA spaces. Source code:https://github.com/marsggbo/NAS-LID.

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Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification

Cited by 4 publications

References 33 publications

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

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