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Trusting the predictions of deep learning models in safety critical settingssuch as the medical domain is still not a viable option. Distentangleduncertainty quantification in the field of medical imaging has received littleattention. In this paper, we study disentangled uncertainties in image to imagetranslation tasks in the medical domain. We compare multiple uncertaintyquantification methods, namely Ensembles, Flipout, Dropout, and DropConnect,while using CycleGAN to convert T1-weighted brain MRI scans to T2-weightedbrain MRI scans. We further evaluate uncertainty behavior in the presence ofout of distribution data (Brain CT and RGB Face Images), showing that epistemicuncertainty can be used to detect out of distribution inputs, which shouldincrease reliability of model outputs.

Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models