Semi-supervised Rare Disease Detection Using Generative Adversarial Network
This addresses the challenge of detecting rare diseases with limited labeled data, which is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of rare disease detection by developing a semi-supervised learning framework using generative adversarial networks, which achieved the best precision-recall score compared to baseline techniques.
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.