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Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
Recent advances in deep learning methods have come to define the
state-of-the-art for many medical imaging applications, surpassing even human
judgment in several tasks. Those models, however, when trained to reduce the
empirical risk on a single domain, fail to generalize when applied to other
domains, a very common scenario in medical imaging due to the variability of
images and anatomical structures, even across the same imaging modality. In
this work, we extend the method of unsupervised domain adaptation using
self-ensembling for the semantic segmentation task and explore multiple facets
of the method on a small and realistic publicly-available magnetic resonance
(MRI) dataset. Through an extensive evaluation, we show that self-ensembling
can indeed improve the generalization of the models even when using a small
amount of unlabelled data.Comment: 15 pages, 9 figure
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