7 citations · 8 across the 3 of their papers we have counts for
3 papers
Robustifying pathology foundation models via fine-tuning
Alexandre Filiot, Oskar Thaeter, Benoit Schmauch +1
Pathology foundation models (FMs) produce powerful tile-level representations which remain sensitive to scanner and staining variability, undermining deployment across laboratories…
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images
Thiziri Nait Saada, Valentina Di Proietto, Benoit Schmauch +2
Multiple Instance Learning (MIL) models have proven effective for cancer prognosis from Whole Slide Images. However, the original MIL formulation incorrectly assumes the patches of…
Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers
Charlie Saillard, Olivier Dehaene, Tanguy Marchand +4
Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in…