Automatic detection of retinopathy with optical coherence tomography images via a semi-supervised deep learning method

Automatic detection of retinopathy via computer vision techniques is of great importance for clinical applications. However, traditional deep learning based methods in computer vision require a large amount of labeled data, which are expensive and may not be available in clinical applications. To mitigate this issue, in this paper, we propose a semi-supervised deep learning method built upon pre-trained VGG-16 and virtual adversarial training (VAT) for the detection of retinopathy with optical coherence tomography (OCT) images. It only requires very few labeled and a number of unlabeled OCT images for model training. In experiments, we have evaluated the proposed method on two popular datasets. With only 80 labeled OCT images, the proposed method can achieve classification accuracies of 0.942 and 0.936, sensitivities of 0.942 and 0.936, specificities of 0.971 and 0.979, and AUCs (Area under the ROC Curves) of 0.997 and 0.993 on the two datasets, respectively. When comparing with human experts, it achieves expert level with 80 labeled OCT images and outperforms four out of six experts with 200 labeled OCT images. Furthermore, we also adopt the Gradient Class Activation Map (Grad-CAM) method to visualize the key regions that the proposed method focuses on when making predictions. It shows that the proposed method can accurately recognize the key patterns of the input OCT images when predicting retinopathy.