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Contrastive self-supervised learning for diabetic retinopathy early detection

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Abstract

Diabetic Retinopathy (DR) is the major cause of blindness, which seriously threatens the world’s vision health. Limited medical resources make early diagnosis and a large-scale screening of DR difficult. Most of the current automatic diagnostic methods are mostly based on deep learning and large-scale labeled data. However, the insufficiency of manual annotations for medical images still is a great challenge of training deep neural networks. Self-supervised learning methods are proposed to learn general features from dataset without manual annotations. Inspired by this, we proposed a deep learning based DR classification model (SimCLR-DR). In this paper, we first use contrastive self-learning algorithm to pre-train the encoder based on convolution network with unlabeled retinal images, then retrain the encoder with classifier on a small annotated training data to detect referable DR. The experimental results on Kaggle dataset show that this proposed method can overcome the training data insufficiency problem and performs better than transfer learning. SimCLR-DR is a good beginning for other deep learning based medical image detection approaches facing the challenge of insufficient annotated data.

Graphical abstract

Figure presents an overview of the proposed framework, which contains three main steps: (i) Data preprocessing; (ii) Pretext task of SimCLR-DR based on contrastive learning; (iii) Downstream Task of SimCLRDR based on CNN.

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Data Availability

Data will be made available on reasonable request.

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Acknowledgements

We would like to acknowledge support for this project from the National Natural Science Foundation of China(NSFC) (No. 61876071, No. 62006094), the Scientific and Technological Developing Scheme of Jilin Province of China (No. 20180201003SF, No. 20190701031GH) and the Energy Administration of Jilin Province (No.3D516L921421).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Jilin University, Qianjin Street, Changchun, 130015, Jilin Province, China

    Jihong Ouyang, Dong Mao, Zeqi Guo & Siguang Liu

  2. University of Missouri, Columbia, MO, USA

    Dong Xu

  3. Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA

    Wenting Wang

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  1. Jihong Ouyang
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  2. Dong Mao
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  4. Siguang Liu
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Correspondence to Siguang Liu.

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Ouyang, J., Mao, D., Guo, Z. et al. Contrastive self-supervised learning for diabetic retinopathy early detection. Med Biol Eng Comput 61, 2441–2452 (2023). https://doi.org/10.1007/s11517-023-02810-5

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