Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders

David Zimmerer, Fabian Isensee, Jens Petersen, Simon Kohl, Klaus H. Maier-Hein

Abstract: An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious.