  Anomaly detection and localization in medical images is a challenging task,
especially when the anomaly exhibits a change of existing structures, e.g.,
brain atrophy or changes in the pleural space due to pleural effusions. In this
work, we present a weakly supervised and detail-preserving method that is able
to detect structural changes of existing anatomical structures. In contrast to
standard anomaly detection methods, our method extracts information about the
disease characteristics from two groups: a group of patients affected by the
same disease and a healthy control group. Together with identity-preserving
mechanisms, this enables our method to extract highly disease-specific
characteristics for a more detailed detection of structural changes. We
designed a specific synthetic data set to evaluate and compare our method
against state-of-the-art anomaly detection methods. Finally, we show the
performance of our method on chest X-ray images. Our method called DeScarGAN
outperforms other anomaly detection methods on the synthetic data set and by
visual inspection on the chest X-ray image data set.
