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Supervised learning algorithms trained on medical images will often fail togeneralize across changes in acquisition parameters. Recent work in domainadaptation addresses this challenge and successfully leverages labeled data ina source domain to perform well on an unlabeled target domain. Inspired byrecent work in semi-supervised learning we introduce a novel method to adaptfrom one source domain to $n$ target domains (as long as there is paired datacovering all domains). Our multi-domain adaptation method utilises aconsistency loss combined with adversarial learning. We provide results onwhite matter lesion hyperintensity segmentation from brain MRIs using theMICCAI 2017 challenge data as the source domain and two target domains. Theproposed method significantly outperforms other domain adaptation baselines.

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning