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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.

Maximilian Alber, Stephan Tietz, Jonas Dippel, Timo Milbich, Timoth\'ee Lesort, Panos Korfiatis, Moritz Kr\"ugener, Beatriz Perez Cancer, Neelay Shah, Alexander M\"ollers, Philipp Seegerer, Alexandra Carpen-Amarie, Kai Standvoss, Gabriel Dernbach, Edwin de Jong, Simon Schallenberg, Andreas Kunft, Helmut Hoffer von Ankershoffen, Gavin Schaeferle, Patrick Duffy, Matt Redlon, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert M\"uller, Frederick Klauschen, Andrew Norgan• 2025

Related benchmarks

TaskDatasetResultRank
General Histopathology PerformancePatho-Bench Overall 1.0 (test)
Prediction Average57
5
Molecular predictionPatho-Bench Molecular 1.0 (test)
TP53 Mutation AUC (BRCA)78.2
5
Treatment Response PredictionPatho-Bench Treatment Response 1.0 (test)
ER Status (Macro OvR AUC)69.2
5
Survival PredictionPatho-Bench Survival 1.0 (test)
Ovary C-Index49.5
5
Morphology PredictionPatho-Bench Morphology 1.0 (test)
TME Immune Class (BRCA) BA51.7
5
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