Lesion-Aware Contrastive Learning for Diabetic Retinopathy Diagnosis

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 .