Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study

Christoph Baur, Stefan Denner, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni

Abstract: Highlights•A comparative study of recent Autoencoder-based Unsupervised Anomaly Detection methods.•A unified network architecture for a valid comparison of all the reviewed methods and models.•Investigations of Unsupervised Anomaly Detection performances on different pathologies.•Sensitivity of reviewed methods to domain shift when working with MR images from different scanners and sites.•Amount of training data and its impact on anomaly detection performance.