  Machine learning (ML) algorithms are optimized for the distribution
represented by the training data. For outlier data, they often deliver
predictions with equal confidence, even though these should not be trusted. In
order to deploy ML-based digital pathology solutions in clinical practice,
effective methods for detecting anomalous data are crucial to avoid incorrect
decisions in the outlier scenario. We propose a new unsupervised learning
approach for anomaly detection in histopathology data based on generative
adversarial networks (GANs). Compared to the existing GAN-based methods that
have been used in medical imaging, the proposed approach improves significantly
on performance for pathology data. Our results indicate that histopathology
imagery is substantially more complex than the data targeted by the previous
methods. This complexity requires not only a more advanced GAN architecture but
also an appropriate anomaly metric to capture the quality of the reconstructed
images.
