Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation. (arXiv:2209.07910v1 [cs.CV])

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source …

arxiv consistent image medical memory segmentation unsupervised