Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

5 Jul 2019Cheng OuyangKonstantinos KamnitsasCarlo BiffiJinming DuanDaniel Rueckert

Deep learning models trained on medical images from a source domain (e.g. imaging modality) often fail when deployed on images from a different target domain, despite imaging common anatomical structures. Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data... (read more)

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