RudolfV: A Foundation Model by Pathologists for Pathologists
This work addresses the problem of improving AI generalization and application variety in computational pathology for pathologists and researchers, though it is incremental by building on existing foundation model approaches.
The study tackled the limitations of current AI models in computational pathology, such as poor generalization and handling rare diseases, by developing RudolfV, a foundation model that incorporates pathologist expertise and a diverse dataset; it demonstrated superior performance over state-of-the-art models in benchmarks for tumor microenvironment profiling, biomarker evaluation, and reference case search.
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.