%0 Generic
%A Zimmerer, David
%C Heidelberg
%D 2022
%F heidok:32524
%R 10.11588/heidok.00032524
%T Unsupervised Learning for Anomaly Detection in Medical Images
%U https://archiv.ub.uni-heidelberg.de/volltextserver/32524/
%X Anomaly detection and localization can learn what data looks like and point out anomalous data samples, which may then be utilized to assist clinicians in identifying anomalies. We employ a Variational Autoencoder (VAE) to learn the distribution of the data and demonstrate several ways for highlighting abnormalities.  We show that using self-supervised learning and hierarchical representations can increase performance, especially in situations with smaller and more difficult-to-detect cases. We further investigate the approaches’ performance and assessment  in two contexts: an international public competitive setting and a real-world use case for discovering incidental findings in a population study. Overall, the results  are encouraging, and the algorithms can detect anomalies and incidental findings, but they fall short in more complex and difficult cases and are not yet dependable enough for real-world usage.