MPAMatch combines a UNI-based encoder, UniMatch-style consistency, and image/text prototype contrastive losses to improve semi-supervised pathology segmentation, reporting state-of-the-art results on four public datasets.
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,
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Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation
MPAMatch combines a UNI-based encoder, UniMatch-style consistency, and image/text prototype contrastive losses to improve semi-supervised pathology segmentation, reporting state-of-the-art results on four public datasets.