Evidential Prototype Learning for Semi-supervised Medical Image Segmentation

The proposed Evidential Prototype Learning (EPL) framework extends the probabilistic framework by incorporating multi-objective sets into evidential deep learning, employs Dempster's combination rule for fusing evidential multi-classifier predictions, integrates belief entropy for dual uncertainty measurement, and guides learning through uncertainty in labeled and unlabeled data, thereby improving prediction accuracy and credibility allocation.