3 papers
cs.CV2026
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
Carlijn Lems, Sander Moonemans, Natálie KlubÃÄková +53
Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its pote…
eess.IV2025
Foundation Model-Driven Classification of Atypical Mitotic Figures with Domain-Aware Training Strategies
Piotr Giedziun, Jan SoÅtysik, Mateusz Górczany +7
We present a solution for the MIDOG 2025 Challenge Track~2, addressing binary classification of normal mitotic figures (NMFs) versus atypical mitotic figures (AMFs). The approach l…
eess.IV2025
RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach
Piotr Giedziun, Jan SoÅtysik, Mateusz Górczany +7
Mitotic figure detection in histopathology images remains challenging due to significant domain shifts across different scanners, staining protocols, and tissue types. This paper p…