RudolfV: A Foundation Model by Pathologists for Pathologists

TL;DR

RudolfV is a pathologist-designed foundation model for computational pathology that leverages domain expertise, diverse datasets, and semi-automated curation to improve generalization, robustness, and application scope in histopathology AI.

Contribution

The paper introduces RudolfV, a novel foundation model integrating pathologist knowledge, diverse data, and semi-automated curation, advancing AI capabilities in computational pathology.

Findings

Outperforms existing models on tumor microenvironment profiling

Shows improved robustness and application versatility

Enables novel pathology applications

Abstract

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.