Mutual Consistency Learning for Semi-supervised Medical Image Segmentation. (arXiv:2109.09960v3 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2109.09960

In this paper, we propose a novel mutual consistency network (MC-Net+) to
effectively exploit the unlabeled data for semi-supervised medical image
segmentation. The MC-Net+ model is motivated by the observation that deep
models trained with limited annotations are prone to output highly uncertain
and easily mis-classified predictions in the ambiguous regions (e.g., adhesive
edges or thin branches) for medical image segmentation. Leveraging these
challenging samples can make the semi-supervised segmentation model training
more effective. Therefore, our proposed MC-Net+ model consists …

arxiv cv image learning medical segmentation semi-supervised