  Medical images are often used to detect and characterize pathology and
disease; however, automatically identifying and segmenting pathology in medical
images is challenging because the appearance of pathology across diseases
varies widely. To address this challenge, we propose a Bayesian deep learning
method that learns to translate healthy computed tomography images to magnetic
resonance images and simultaneously calculates voxel-wise uncertainty. Since
high uncertainty occurs in pathological regions of the image, this uncertainty
can be used for unsupervised anomaly segmentation. We show encouraging
experimental results on an unsupervised anomaly segmentation task by combining
two types of uncertainty into a novel quantity we call scibilic uncertainty.
