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
Identifying actionable driver mutations in lung cancer using an efficient Asymmetric Transformer Decoder
Biagio Brattoli, Jack Shi, Jongchan Park +3
Identifying actionable driver mutations in non-small cell lung cancer (NSCLC) can impact treatment decisions and significantly improve patient outcomes. Despite guideline recommend…
q-bio.QM2025
Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss
Gianluca Carloni, Biagio Brattoli, Seongho Keum +4
Computational pathology (CPath) has shown great potential in mining actionable insights from Whole Slide Images (WSIs). Deep Learning (DL) has been at the center of modern CPath, a…