e-UDA: Efficient Unsupervised Domain Adaptation for Cross-Site Medical Image Segmentation
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Domain adaptation in healthcare data is a potentially critical component in making computer-aided diagnostic systems applicable cross multiple sites and imaging scanners. In this paper, we propose an efficient unsupervised domain adaptation framework for robust image segmentation cross multiple similar domains. We enforce our algorithm to not only adapt to the new domains via an adversarial optimization, rejecting unlikely segmentation patterns, but also to maintain its performance on the source training data, by incorporating both semantic and boundary information into the data distributions. Further, as we do not have labels for the transfer domain, we propose a new quality score for the adaptation process, and strategies to retrain the diagnostic algorithm in a stable fashion. Using multi-centric data from a public benchmark for brain lesion segmentation, we demonstrate that recalibrating on just few unlabeled image sets from the target domain improves segmentation accuracies drastically, with performances almost similar to those from algorithms trained on fresh and fully annotated data from the test domain.
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