Intelligent Glaucoma Diagnosis Via Active Learning And Adversarial Data Augmentation

Glaucoma is the leading causes of blindness in the world. We develop a convolutional neural network for glaucoma diagnosis based on visual fields (VF), which is the gold standard to show functional damages of optic nerve. However, we have to deal with two major problems common in medical imaging domains. 1) It is difficult and expensive to label a large amount of data, while most modern deep learning methods require it. 2) Severe data imbalance makes the classifier easily over-fitting. In this work, for the first problem, we train an AutoEncoder with all the data (labeled and unlabeled) to obtain good features and introduce an active learning (AL) scheme to select and annotate a few most valuable samples from the unlabeled date set for model training. Then, we address the second problem by augmenting negative samples generated by a deep convolutional generative adversarial network (DCGAN). Experiments on our dataset (738 Samples) suggest the effectiveness of the proposed approach.