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Semi-supervised medical image classification via increasing prediction diversity

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Abstract

Deep learning models have achieved remarkable success in medical imaging analysis. However, existing methods are primarily focused on supervised learning, which requires a massive amount of training data. Recent studies have explored semi-supervised learning approaches to address this issue, where data augmentation was applied to unlabeled data. However, there are still two unsolved challenges in applying data augmentation to unlabeled medical images: it can i) result in the lesion features loss and ii) reduce the discriminability of prediction results. Thus, in this work, weak data augmentation is applied to unlabeled data to avoid losing lesions features. Also, we propose nuclear-norm maximization to achieve entropy minimization without losing prediction diversity. Experimental results on two public datasets show that the proposed method outperforms the compared models.

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  1. https://www.kaggle.com/obulisainaren/retinal-oct-c8

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Acknowledgments

This work was supported by the Research Foundation of Yunnan Province No. 202002AD080001, 202001BB050043 and 2019FA044, National Natural Science Foundation of China under Grants No.62162065, Provincial Foundation for Leaders of Disciplines in Science and Technology No.2019HB121.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650500, China

    Peng Liu, Wenhua Qian & Dan Xu

  2. School of Mathematics, Southeast University, Nanjing, 210096, China

    Jinde Cao

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  1. Peng Liu
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  2. Wenhua Qian
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Correspondence to Wenhua Qian.

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Liu, P., Qian, W., Cao, J. et al. Semi-supervised medical image classification via increasing prediction diversity. Appl Intell 53, 10162–10175 (2023). https://doi.org/10.1007/s10489-022-04012-2

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