RudolfV: A Foundation Model by Pathologists for Pathologists
arxiv(2024)
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.
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