Self-Supervised Cross-Level Consistency Learning For Fundus Image Classification

The rapid development of intelligent systems for eye disease diagnosis decreases the risk of people suffering from vision impairment. However, the superior discrimination ability of existing retinal disease diagnosis methods heavily relies on the large-scale high-quality annotations. In this work, we adapt the self-supervised technique for fundus image classification with the merits of bypassing the over-dependence of labeled data. Unlike most current self-supervised approaches, which only learn global pre-text representations from view-level, our method further incorporates the region-level representations into the learning process, since the pathological changes in fundus images are usually subtle and scattered. Specifically, we propose a novel self-supervised cross-level consistency learning scheme (S2C2L), which leverages both view-level and region-level representations of a vision Transformer to improve the robustness of extracted self-supervised representation. A diagnosis perception module (DPM) is constructed to enhance the activation of local pathological regions from both region and view levels, and a cross-level consistency loss is dedicated to align the representations from both levels. Extensive experiments on iChallenge-AMD, LAG and APTOS2019 datasets validate the state-of-the-art performance of our method for three common eye diseases.