Abstract
Diabetic Retinopathy (DR) causes quite a few blindness worldwide, which can be refrained by the timely diagnosis on retinal images. Recently, researches on deep learning-based retinal image classification have accelerated outstanding improvements in DR grading task. However, existing DR grading works are mostly limited to a supervised manner. They require accurately annotated data labeled by professional experts, and the annotating work is very laborious and time-consuming. We propose a Semi-supervised Auto-encoder Graph Network (SAGN) for the challenging DR diagnosis to relax this constraint. Precisely, SAGN consists of three major modules: auto-encoder feature learning, neighbor correlation mining, and graph representation. Firstly, our model learns to extract representations from retinal images and reconstruct them as close to original inputs as possible. Then neighbor correlations among labeled and unlabeled samples are established by their similarities, calculated by the radial basis function. Finally, we operate Graph Convolutional Neural Network (GCN) to grade retinal samples from extracted features and their correlations. To evaluate the performance of SAGN, we conduct sufficient comparative experiments on APTOS 2019 dataset, trained from EyePACS. Results demonstrate that our SAGN model can achieve comparable performance with limited labeled retinal images with the help of large amounts of unlabeled data.
Highlights
The retinal blood vascular network is the only vascular network of a human body visible to a non-invasive imaging approach
F (B) into the Graph Convolutional Neural Network (GCN), and output the predicted category Zj ; 6: Compute the semi-supervised cross-entropy loss Lsce by Eq 6; 7: Feed the learned Convolutional Neural Networks (CNNs) features F (B) into the decoder D and compute the reconstruction loss Ldec via Eq(2); 8: Compute the final loss function L = (1 − α)Ldec + αLsce; 9: Optimize the network parameters of CNN encoder, decoder, and GCN according to back-propagation algorithm; 10: until Convergence; Output: The optimized CNN encoder and GCN
EXPERIMENTAL DATASETS EyePACS [28] collects 88,702 annotated colorful fundus images from different patients. These images are captured by different fundus cameras in multiple primary care sites throughout California and elsewhere, and the resolutions are resized to 512 × 512 pixels, categorized into five diabetic retinopathy (DR) grades, including No, Mild, Moderate, Severe, and Proliferative DRs
Summary
The retinal blood vascular network is the only vascular network of a human body visible to a non-invasive imaging approach. Ophthalmologists use color and morphological information to diagnose retinal images into DR grades by discriminating between arteries and veins since arteries contain more oxygen and appear brighter than veins and thinner than neighboring veins [5]. These features of retinal vasculature are usually captured by fundus photography due to its lower cost and ease of use, but manual classification of retinal blood vessels is timeconsuming and subject to human errors. As an advanced technology in machine learning, deep learning-based automatic retinal image classification methods exhibit outstanding DR grading performance, surpassing
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