%V 12261 %S Lecture Notes in Computer Science %D 2020 %C Lima, Peru %K Domain adaptation, Image synthesis, GANs, Segmentation, MRI %B Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 %T Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation %P 510-520 %X The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures. %J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) %A E Chiou %A F Giganti %A S Punwani %A I Kokkinos %A E Panagiotaki %O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. %I Springer %E A.L Martel %E P Abolmaesumi %E D Stoyanov %E D Mateus %E M.A Zuluaga %E S.K Zhou %E D Racoceanu %E L Joskowicz %L discovery10115471