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cs.CV2026
Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
Gbègninougbo Aurel Davy Tchokponhoue, Sevda ÃÄüt, Ali Idri +2
Pathology foundation models (PFMs) offer generalizable representations for whole-slide image (WSI) analysis, yet their clinical adoption remains limited. Specifically, their predic…
cs.CV2026
GrapHist: Graph Self-Supervised Learning for Histopathology
Sevda ÃÄüt, Cédric Vincent-Cuaz, Natalia Dubljevic +4
Self-supervised vision models have achieved notable success in digital pathology. However, their domain-agnostic transformer architectures are not originally designed to account fo…
cs.CV2025
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Boqi Chen, Cédric Vincent-Cuaz, Lydia A. Schoenpflug +12
Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research. These models learn, in a self-supervised manne…