A GI-specific histopathology foundation model pretrained on 210,043 slides with a supervised ROI-mining second stage claims state-of-the-art results on 33 of 34 GI pathology tasks and 99.70 percent screening sensitivity in nine centers.
The ProtoNet first convert all training images into embedding vectors, then performs mean-poolingonembeddingsofthesamecategorytoobtainprototyperepresentations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
A GI-specific histopathology foundation model pretrained on 210,043 slides with a supervised ROI-mining second stage claims state-of-the-art results on 33 of 34 GI pathology tasks and 99.70 percent screening sensitivity in nine centers.