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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 8 Pith papers
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From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology
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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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Towards Robust Foundation Models for Digital Pathology
PathoROB shows that all 20 evaluated pathology foundation models encode medical center information and that lower robustness correlates with larger downstream performance drops.
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PhenoBench: A Comprehensive Benchmark for Cell Phenotyping
A new H&E benchmark with 14 fine-grained cell types and biological domain splits shows pathology foundation models scoring around 0.20 to 0.28 macro F1, far below their near-saturated performance on older benchmarks.
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MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification
Conditioning a histopathology diffusion model on tissue source site metadata improves synthetic image fidelity and enhances downstream tumor classification under subpopulation shift.
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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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Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
JWTH achieves modest tissue-classification gains by adding attention pooling and stain augmentation to a DINOv3 backbone, but the biomarker claims in the abstract are unsupported by the experiments.
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A review that groups AI methods for cancer spatial omics into data-driven, constraint-based, and mechanistic modeling paradigms, calling for more interpretable models and mouse-model-generated perturbational data.
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