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Diabetic retinal image classification aims to conduct diabetic retinopathy automatically diagnosing, which has achieved considerable improvement by deep learning models. However, these methods all rely on sufficient network training by large scale annotated data, which is very labor-expensive in medical image labeling. Aiming to overcome these drawbacks, this paper focuses on embedding self-supervised framework into unsupervised deep learning architecture. Specifically, we propose adoi:10.1109/access.2020.2994047 fatcat:t77mpusgerb5tb2g7vuitxdseq