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.
Semi- supervised semantic segmentation with prototype-based consistency regularization.Advances in neural information processing systems, 35:26007–26020, 2022
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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.