most citedUnsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

148 citations · 148 across the 1 of their papers we have counts for

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1 paper

cs.CV2017★ 148 cited

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein +2

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with ann…