  Manually annotating medical images is extremely expensive, especially for
large-scale datasets. Self-supervised contrastive learning has been explored to
learn feature representations from unlabeled images. However, unlike natural
images, the application of contrastive learning to medical images is relatively
limited. In this work, we propose a self-supervised framework, namely
lesion-based contrastive learning for automated diabetic retinopathy (DR)
grading. Instead of taking entire images as the input in the common contrastive
learning scheme, lesion patches are employed to encourage the feature extractor
to learn representations that are highly discriminative for DR grading. We also
investigate different data augmentation operations in defining our contrastive
prediction task. Extensive experiments are conducted on the publicly-accessible
dataset EyePACS, demonstrating that our proposed framework performs
outstandingly on DR grading in terms of both linear evaluation and transfer
capacity evaluation.
