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MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification

Lu, Liangfu; Cui, Xudong; Tan, Zhiyuan; Wu, Yulei

Authors

Liangfu Lu

Xudong Cui

Yulei Wu



Abstract

In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image classification. The framework enables the use of various high-performance convex optimization models as classifiers, such as multi-class kernel support vector machines, ridge regression, and other models. End-to-end training is then implemented using dual problems and differentiation in the paper. Additionally, various regularization techniques are employed to enhance the model's generalization capabilities. Experiments on the BreakHis, ISIC2018, and Pap smear medical few-shot datasets demonstrate that the MedOptNet framework outperforms benchmark models. Moreover, the model training time is also compared to prove its effectiveness in the paper, and an ablation study is conducted to validate the effectiveness of each module.

Journal Article Type Article
Acceptance Date May 29, 2023
Online Publication Date Jun 12, 2023
Deposit Date Jun 9, 2023
Publicly Available Date Jun 12, 2023
Print ISSN 1545-5963
Electronic ISSN 1557-9964
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TCBB.2023.3284846
Keywords few-shot, meta learning, convex optimization, medical image classification
Publisher URL https://dl.acm.org/journal/tcbb

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MedOptNet: Meta-Learning Framework For Few-shot Medical Image Classification (accepted version) (1.9 Mb)
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