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.
@article{arxiv.2601.05148,
title = {Atlas 2 -- Foundation models for clinical deployment},
author = {Maximilian Alber and Timo Milbich and Alexandra Carpen-Amarie and Stephan Tietz and Jonas Dippel and Lukas Muttenthaler and Beatriz Perez Cancer and Alessandro Benetti and Panos Korfiatis and Elias Eulig and Jérôme Lüscher and Jiasen Wu and Sayed Abid Hashimi and Gabriel Dernbach and Simon Schallenberg and Neelay Shah and Moritz Krügener and Aniruddh Jammoria and Jake Matras and Patrick Duffy and Matt Redlon and Philipp Jurmeister and David Horst and Lukas Ruff and Klaus-Robert Müller and Frederick Klauschen and Andrew Norgan},
journal= {arXiv preprint arXiv:2601.05148},
year = {2026}
}