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Atlas 2 -- Foundation models for clinical deployment

About

Pathology foundation models substantially advanced the possibilities in computational pathology -- yet tradeoffs in terms of performance, robustness, and computational requirements remained, which limited their clinical deployment. In this report, we present Atlas 2, Atlas 2-B, and Atlas 2-S, three pathology vision foundation models which bridge these shortcomings by showing state-of-the-art performance in prediction performance, robustness, and resource efficiency in a comprehensive evaluation across eighty public benchmarks. Our models were trained on the largest pathology foundation model dataset to date comprising 5.5 million histopathology whole slide images, collected from three medical institutions Charit\'e - Universt\"atsmedizin Berlin, LMU Munich, and Mayo Clinic.

Maximilian Alber, Timo Milbich, Alexandra Carpen-Amarie, Stephan Tietz, Jonas Dippel, Lukas Muttenthaler, Beatriz Perez Cancer, Alessandro Benetti, Panos Korfiatis, Elias Eulig, J\'er\^ome L\"uscher, Jiasen Wu, Sayed Abid Hashimi, Gabriel Dernbach, Simon Schallenberg, Neelay Shah, Moritz Kr\"ugener, Aniruddh Jammoria, Jake Matras, Patrick Duffy, Matt Redlon, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert M\"uller, Frederick Klauschen, Andrew Norgan• 2026

Related benchmarks

TaskDatasetResultRank
General Histopathology PerformancePatho-Bench Overall 1.0 (test)
Prediction Average60.3
5
Molecular predictionPatho-Bench Molecular 1.0 (test)
TP53 Mutation AUC (BRCA)83.6
5
Morphology PredictionPatho-Bench Morphology 1.0 (test)
TME Immune Class (BRCA) BA59.3
5
Survival PredictionPatho-Bench Survival 1.0 (test)
Ovary C-Index51.7
5
Treatment Response PredictionPatho-Bench Treatment Response 1.0 (test)
ER Status (Macro OvR AUC)76.4
5
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