PaRCL: Pathology-aware Representation Contrastive Learning for Glaucoma Classification on Fundus Images

Recently, there has been a growing interest in applying self-supervised contrastive learning to medical image classification tasks. In this paper, we investigate its application for glaucoma classification using fundus images. Given the highly detailed features in fundus images, the effectiveness of contrastive learning, particularly when data augmentation is used to create contrastive pairs, proves to be notably limited. This constraint impairs the extraction of fine-grained visual representations on fundus images, resulting in reduced performance of self-supervised contrastive learning for glaucoma classification. To alleviate the above issues, we propose a pathology-aware representations contrastive learning (PaRCL) method, which learns more discriminative fine-grained representations compared to the previous self-supervised contrastive learning methods, to enhance the model performance for the glaucoma classification effectively. Furthermore, our method could focus more on the lesion area and effectively extract interpretable classification features. Particularly, to fully learn more discriminative visual representations, we introduce a pathology-aware subnetwork ensemble strategy (SES) as an alternative to generate contrastive pairs through traditional data augmentation techniques. Experiments on three clinical datasets demonstrate that our PaRCL could improve the performance of glaucoma classification on fundus images and could effectively focus on pathology areas of fundus images for disease diagnosis.