Federated Semi-supervised Medical Image Segmentation via Prototype-based Pseudo-labeling and Contrastive Learning
Description
Existing federated learning works mainly focus on the fully supervised training setting. In realistic scenarios, however, most clinical sites can only provide data without annotations due to the lack of resources or expertise. In this work, we are
