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.
Adaptive feature aggregation based multi-task learning for uncertainty-guided semi-supervised medical image segmentation.Expert Systems with Applications, 232:120836, 2023
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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.