MPCL uses intensity-aligned heterogeneous prototype generation, prototypical space optimization, and dual-branch knowledge alignment to improve semi-supervised medical image segmentation by modeling intra-class heterogeneity.
D-edl: Differ- ential evidential deep learning for robust medical out-of-distribution detection.Medical Image Analysis, page 103888, 2025
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Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision
MPCL uses intensity-aligned heterogeneous prototype generation, prototypical space optimization, and dual-branch knowledge alignment to improve semi-supervised medical image segmentation by modeling intra-class heterogeneity.