Semi-supervised Rare Disease Detection Using Generative Adversarial Network
arXiv:1812.00547
Abstract
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.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
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Cited by in corpus (5)
- A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
- SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
- Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
- Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
- Semi-supervised Learning using Adversarial Training with Good and Bad Samples