Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation. (arXiv:2112.02508v2 [eess.IV] UPDATED)

Medical image segmentation is a fundamental and critical step in many
clinical approaches. Semi-supervised learning has been widely applied to
medical image segmentation tasks since it alleviates the heavy burden of
acquiring expert-examined annotations and takes the advantage of unlabeled data
which is much easier to acquire. Although consistency learning has been proven
to be an effective approach by enforcing an invariance of predictions under
different distributions, existing approaches cannot make full use of
region-level shape constraint and boundary-level distance …

arxiv image learning medical segmentation semi-supervised uncertainty