  Rare diseases affect a relatively small number of people, which limits
investment in research for treatments and cures. Developing an efficient method
for rare disease detection is a crucial first step towards subsequent clinical
research. In this paper, we present a semi-supervised learning framework for
rare disease detection using generative adversarial networks. Our method takes
advantage of the large amount of unlabeled data for disease detection and
achieves the best results in terms of precision-recall score compared to
baseline techniques.
