Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
Maximilian Alber
Stephan Tietz
Jonas Dippel
Timo Milbich
Timothée Lesort
Panos Korfiatis
Moritz Krügener
Beatriz Perez Cancer
Neelay Shah
Alexander Möllers
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 Müller
Frederick Klauschen
Andrew Norgan
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 Charité - Universtätsmedizin 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.
