Abstract:Deep neural networks are very compelling for medical image segmentation. However, deep models often suffer from notable performance drops in real clinical settings due to the complex appearance shift in daily scannings. Domain adaptation partially addresses the problem between imaging domains. However, it heavily depends on the expensive recollection and retraining for domain-specific datasets and thus is not applicable to domain-agnostic images. In this paper, we propose a case adaptation strategy aiming to b… Show more
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