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RudolfV: A Foundation Model by Pathologists for Pathologists
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RudolfV: A Foundation Model by Pathologists for Pathologists
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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.
Forward citations
Cited by 7 Pith papers
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Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
An Atlas-foundation-model system for multi-cancer H&E tissue and cell profiling matches pathologist H&E accuracy against IHC-informed consensus and generalizes across 1,500+ cases.
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Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
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Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Atlas H&E-TME is a new AI system for cell-level tissue profiling on H&E slides that matches pathologist performance when validated against an IHC-informed consensus and a large multi-cancer H&E annotation set.
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Atlas 2 -- Foundation models for clinical deployment
Atlas 2 and its distilled variants set new average state-of-the-art results across 80 pathology benchmarks, with larger robustness margins over prior models.
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OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
OpenTME provides pre-computed TME profiles with over 4,500 quantitative readouts per slide from 3,634 TCGA H&E images using an AI pipeline based on pathology foundation models.
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From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research
A review mapping the transition from classical machine learning to foundation models for multimodal data integration in cancer research.
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Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
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