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Lesion-Aware Contrastive Learning for Diabetic Retinopathy Diagnosis

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14226))

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Abstract

Early diagnosis and screening of diabetic retinopathy are critical in reducing the risk of vision loss in patients. However, in a real clinical situation, manual annotation of lesion regions in fundus images is time-consuming. Contrastive learning(CL) has recently shown its strong ability for self-supervised representation learning due to its ability of learning the invariant representation without any extra labelled data. In this study, we aim to investigate how CL can be applied to extract lesion features in medical images. However, can the direct introduction of CL into the deep learning framework enhance the representation ability of lesion characteristics? We show that the answer is no. Due to the lesion-specific regions being insignificant in medical images, directly introducing CL would inevitably lead to the effects of false negatives, limiting the ability of the discriminative representation learning. Essentially, two key issues should be considered: (1) How to construct positives and negatives to avoid the problem of false negatives? (2) How to exploit the hard negatives for promoting the representation quality of lesions? In this work, we present a lesion-aware CL framework for DR grading. Specifically, we design a new generating positives and negatives strategy to overcome the false negatives problem in fundus images. Furthermore, a dynamic hard negatives mining method based on knowledge distillation is proposed in order to improve the quality of the learned embeddings. Extensive experimental results show that our method significantly advances state-of-the-art DR grading methods to a considerable 88.0%ACC/86.8% Kappa on the EyePACS benchmark dataset. Our code is available at https://github.com/IntelliDAL/Image.

S. Cheng and Q. Ho—Contribute equally to this work.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (No.62076059), the Science Project of Liaoning province under Grant (2021-MS-105) and the 111 Project (B16009).

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Authors and Affiliations

  1. Computer Science and Engineering, Northeastern University, Shenyang, China

    Shuai Cheng, Qingshan Hou, Peng Cao & Jinzhu Yang

  2. Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China

    Shuai Cheng, Qingshan Hou, Peng Cao & Jinzhu Yang

  3. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, 110819, China

    Peng Cao & Jinzhu Yang

  4. DAMO Academy, Alibaba Group, Hangzhou, China

    Xiaoli Liu

  5. Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada

    Osmar R. Zaiane

Authors
  1. Shuai Cheng
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  2. Qingshan Hou
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  3. Peng Cao
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  4. Jinzhu Yang
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  5. Xiaoli Liu
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  6. Osmar R. Zaiane
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Corresponding authors

Correspondence to Peng Cao or Jinzhu Yang .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Cheng, S., Hou, Q., Cao, P., Yang, J., Liu, X., Zaiane, O.R. (2023). Lesion-Aware Contrastive Learning for Diabetic Retinopathy Diagnosis. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_63

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  • DOI: https://doi.org/10.1007/978-3-031-43990-2_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43989-6

  • Online ISBN: 978-3-031-43990-2

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