  This work considers semi-supervised segmentation as a dense prediction
problem based on prototype vector correlation and proposes a simple way to
represent each segmentation class with multiple prototypes. To avoid degenerate
solutions, two regularization strategies are applied on unlabeled images. The
first one leverages mutual information maximization to ensure that all
prototype vectors are considered by the network. The second explicitly enforces
prototypes to be orthogonal by minimizing their cosine distance. Experimental
results on two benchmark medical segmentation datasets reveal our method's
effectiveness in improving segmentation performance when few annotated images
are available.
