Bayesian Skip-Autoencoders for Unsupervised Hyperintense Anomaly Detection in High Resolution Brain Mri

Autoencoder-based approaches for Unsupervised Anomaly Detection (UAD) in brain MRI have recently gained a lot of attention and have shown promising performance. However, brain MR images are particularly complex and require large model capacity for learning a proper reconstruction, which existing methods encounter by restricting themselves to downsampled data or anatomical subregions. In this work, we show that models with limited capacity can be trained and used for UAD in full brain MR images at their native resolution by introducing skip-connections, a concept which has already proven beneficial for biomedical image segmentation and image-to-image translation, and a dropout-based mechanism to prevent the model from learning an identity mapping. In an ablative study on two different pathologies we show considerable improvements over State-of-the-Art Autoencoder-based UAD models. The stochastic nature of the model also allows to investigate epistemic uncertainty in our so-called Skip-Autoencoder, which is briefly portrayed.