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arxiv: 2401.04079 · v4 · pith:EC2JUPI2 · submitted 2024-01-08 · eess.IV · cs.CV· cs.LG

RudolfV: A Foundation Model by Pathologists for Pathologists

Jonas Dippel , Barbara Feulner , Tobias Winterhoff , Timo Milbich , Stephan Tietz , Simon Schallenberg , Gabriel Dernbach , Andreas Kunft
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Simon Heinke Marie-Lisa Eich Julika Ribbat-Idel Rosemarie Krupar Philipp Anders Niklas Preni{\ss}l Philipp Jurmeister David Horst Lukas Ruff Klaus-Robert M\"uller Frederick Klauschen Maximilian Alber
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classification eess.IV cs.CVcs.LG
keywords foundationmodelspathologyapplicationapproachescomputationaldifferentexisting
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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.

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