Abstract
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.
Highlights
Retinal images classification of diabetic retinopathy is a significant application in diagnosing diabetic retinopathy
In order to overcome this limitation, this paper propose a Selfsupervised Fuzzy Clustering Network (SFCN) to conduct retinal image classification without any annotations
Gidaris et al [19] proposed to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input (RotNet); Feng et al [15] introduced a self-supervised learning method that incorporates rotation invariance into the feature learning framework, one of many good and well-studied properties of visual representation, which is rarely appreciated or exploited by previous deep convolutional neural network based selfsupervised representation learning methods
Summary
Retinal images classification of diabetic retinopathy is a significant application in diagnosing diabetic retinopathy. Bourouis et al [9] proposed a robust hybrid probabilistic learning approach that appropriately combines the advantages both of the generative and discriminative model for challenging problem in retinal image classification It obtained a better results and showed the flexibility and the merits of deep learning applications in retinal image analysis. Though sufficient training on large amount of annotated data, existing deep learning based retinal image classification methods have achieved satisfactory performance according to the review research [33], where more successful applications can be found. There is a main drawback in existing deep learning retinal image diagnose works That is, they require large amounts of labeled data to supervise the network learning. SFCN employs the fuzzy clustering results to supervise the network and constrain the network to output the probability of each retinal image belonging every clusters
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