Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation. (arXiv:2203.01324v2 [eess.IV] UPDATED)

Web: http://arxiv.org/abs/2203.01324

Semi-supervised segmentation remains challenging in medical imaging since the
amount of annotated medical data is often limited and there are many blurred
pixels near the adhesive edges or low-contrast regions. To address the issues,
we advocate to firstly constrain the consistency of samples with and without
strong perturbations to apply sufficient smoothness regularization and further
encourage the class-level separation to exploit the unlabeled ambiguous pixels
for the model training. Particularly, in this paper, we propose the SS-Net for
semi-supervised medical …

arxiv image medical segmentation semi-supervised