Atlas 2 -- Foundation models for clinical deployment

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Authors: Maximilian Alber, Timo Milbich, Alexandra Carpen-Amarie, Stephan Tietz, Jonas Dippel, Lukas Muttenthaler, Beatriz Perez Cancer, Alessandro Benetti, Panos Korfiatis, Elias Eulig, Jérôme Lüscher, Jiasen Wu
+15 more Sayed Abid Hashimi, Gabriel Dernbach, Simon Schallenberg, Neelay Shah, Moritz Krügener, Aniruddh Singh Jammoria, Jake Matras, Patrick Duffy, Matt Redlon, Philipp Sebastian Jurmeister, David Horst, Lukas Ruff, Klaus Robert Müller, Frederick Klauschen, Andrew Norgan
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Abstract

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 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 Charité - Universitätsmedizin Berlin, LMU Munich, and Mayo Clinic.

Fields of Study & Subjects

AI in cancer detection Artificial Intelligence in Healthcare and Education Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
arXiv (Cornell University)
Publication date
8 January 2026
Document type
Preprint