  Supervised deep learning algorithms have enabled significant performance
gains in medical image classification tasks. But these methods rely on large
labeled datasets that require resource-intensive expert annotation.
Semi-supervised generative adversarial network (GAN) approaches offer a means
to learn from limited labeled data alongside larger unlabeled datasets, but
have not been applied to discern fine-scale, sparse or localized features that
define medical abnormalities. To overcome these limitations, we propose a
patch-based semi-supervised learning approach and evaluate performance on
classification of diabetic retinopathy from funduscopic images. Our
semi-supervised approach achieves high AUC with just 10-20 labeled training
images, and outperforms the supervised baselines by upto 15% when less than 30%
of the training dataset is labeled. Further, our method implicitly enables
interpretation of the SSL predictions. As this approach enables good accuracy,
resolution and interpretability with lower annotation burden, it sets the
pathway for scalable applications of deep learning in clinical imaging.
