  Semi-supervised learning (SSL) uses unlabeled data to compensate for the
scarcity of annotated images and the lack of method generalization to unseen
domains, two usual problems in medical segmentation tasks. In this work, we
propose POPCORN, a novel method combining consistency regularization and
pseudo-labeling designed for image segmentation. The proposed framework uses
high-level regularization to constrain our segmentation model to use similar
latent features for images with similar segmentations. POPCORN estimates a
proximity graph to select data from easiest ones to more difficult ones, in
order to ensure accurate pseudo-labeling and to limit confirmation bias.
Applied to multiple sclerosis lesion segmentation, our method demonstrates
competitive results compared to other state-of-the-art SSL strategies.
