# Semi-supervised Rare Disease Detection Using Generative Adversarial Network

arXiv: https://arxiv.org/abs/1812.00547 · HTML: https://mlsanity.com/paper/1812.00547/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels](https://mlsanity.com/paper/2208.11290/) (2022)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records](https://mlsanity.com/paper/2401.04402/) (2024)
- [Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection](https://mlsanity.com/paper/2605.06955/) (2026)

## Abstract (arXiv)

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.