  Recently, prototype learning has emerged in semi-supervised medical image
segmentation and achieved remarkable performance. However, the scarcity of
labeled data limits the expressiveness of prototypes in previous methods,
potentially hindering the complete representation of prototypes for class
embedding. To address this problem, we propose the Mixed Prototype Consistency
Learning (MPCL) framework, which includes a Mean Teacher and an auxiliary
network. The Mean Teacher generates prototypes for labeled and unlabeled data,
while the auxiliary network produces additional prototypes for mixed data
processed by CutMix. Through prototype fusion, mixed prototypes provide extra
semantic information to both labeled and unlabeled prototypes. High-quality
global prototypes for each class are formed by fusing two enhanced prototypes,
optimizing the distribution of hidden embeddings used in consistency learning.
Extensive experiments on the left atrium and type B aortic dissection datasets
demonstrate MPCL's superiority over previous state-of-the-art approaches,
confirming the effectiveness of our framework. The code will be released soon.
