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In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e.g. a new centreor a new scanner. In this paper, we propose an unsupervised domain adaptation framework for boosting image segmentation performance across multiple domains without using any manual annotations from the new target domains, but by re-calibrating the networks on fewarXiv:2001.09313v3 fatcat:hyjylq4z6rdcxlkzoz4cmthn6i