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Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics
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Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charit\'e - Universt\"atsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size.
Forward citations
Cited by 3 Pith papers
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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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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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