Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
This work addresses automated medical image grading for diabetic retinopathy, which is incremental as it adapts contrastive learning to a specific medical domain.
The authors tackled diabetic retinopathy grading from fundus images by proposing a lesion-based contrastive learning framework that uses lesion patches instead of entire images, achieving outstanding performance on the EyePACS dataset in linear and transfer evaluations.
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.