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.
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Forward citations
Cited by 2 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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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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