Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to generalization, application variety, and handling rare diseases. Recent efforts introduced self-supervised foundation models to address these challenges, yet existing approaches do not leverage pathologist knowledge by design. In this study, we present a novel approach to designing foundation models for computational pathology, incorporating pathologist expertise, semi-automated data curation, and a diverse dataset from over 15 laboratories, including 58 tissue types, and encompassing 129 different histochemical and immunohistochemical staining modalities. We demonstrate that our model "RudolfV" surpasses existing state-of-the-art foundation models across different benchmarks focused on tumor microenvironment profiling, biomarker evaluation, and reference case search while exhibiting favorable robustness properties. Our study shows how domain-specific knowledge can increase the efficiency and performance of pathology foundation models and enable novel application areas.
@article{arxiv.2401.04079,
title = {RudolfV: A Foundation Model by Pathologists for Pathologists},
author = {Jonas Dippel and Barbara Feulner and Tobias Winterhoff and Timo Milbich and Stephan Tietz and Simon Schallenberg and Gabriel Dernbach and Andreas Kunft and Simon Heinke and Marie-Lisa Eich and Julika Ribbat-Idel and Rosemarie Krupar and Philipp Anders and Niklas Prenißl and Philipp Jurmeister and David Horst and Lukas Ruff and Klaus-Robert Müller and Frederick Klauschen and Maximilian Alber},
journal= {arXiv preprint arXiv:2401.04079},
year = {2024}
}