4 papers
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53
Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its pote…
Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
Sander Moonemans, Sebastiaan Ram, Frédérique Meeuwsen +4
Vision-language models (VLMs) have the potential to become co-pilots for pathologists. However, most VLMs either focus on small regions of interest within whole-slide images, provi…
Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions
Ruben T. Lucassen, Sander P. J. Moonemans, Tijn van de Luijtgaarden +3
Millions of melanocytic skin lesions are examined by pathologists each year, the majority of which concern common nevi (i.e., ordinary moles). While most of these lesions can be di…
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation
Ruben T. Lucassen, Tijn van de Luijtgaarden, Sander P. J. Moonemans +3
Vision-language models in pathology enable multimodal case retrieval and automated report generation. Many of the models developed so far, however, have been trained on pathology r…