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.
Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets.Journal of Pathology Informatics, 14:100174, 2023
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.