K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
Arnold and Emily Zhao and Yilian Yuan , title =
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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.
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A GAN-boosted RNN model reaches 0.56 PR-AUC for rare EPI detection on 1.8 million patients and outperforms benchmarks.
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Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
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Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
A GAN-boosted RNN model reaches 0.56 PR-AUC for rare EPI detection on 1.8 million patients and outperforms benchmarks.