UniSemAlign aligns text and prototype representations with visual features to generate better supervision signals for semi-supervised segmentation, reporting Dice gains of up to 8.6% on CRAG with 10% labels.
Generalization of vision pre-trained models for histopathology.Scientific reports, 13(1):6065
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UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation
UniSemAlign aligns text and prototype representations with visual features to generate better supervision signals for semi-supervised segmentation, reporting Dice gains of up to 8.6% on CRAG with 10% labels.