Retinal Image Classification by Self-Supervised Fuzzy Clustering Network

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 a Self-supervised Fuzzy Clustering Network (SFCN) by a feature learning module, reconstruction module, and a fuzzy self-supervision module. The feature learning and reconstruction modules ensure the representative ability of the network, and fuzzy self-supervision module is in charge of further providing the training direction for the whole network. Furthermore, three losses of reconstruction, self-supervision, and fuzzy supervision jointly optimize the SFCN under an unsupervised manner. To evaluate the effectiveness of the proposed method, we implement the network on three widely used retinal image datasets, which results demonstrate the satisfied performance on unsupervised retinal image classification task.