aiqbench takeMachine Learning

Atlas 2 represents a significant advancement in computational pathology by resolving previous trade-offs between model performance, robustness, and resource efficiency. Trained on an unprecedented dataset of 5.5 million histopathology images from major institutions like Charité and Mayo Clinic, these foundation models now offer state-of-the-art capabilities suitable for actual clinical deployment rather than just research prototypes. This work directly addresses the critical bottleneck preventing widespread adoption of AI in medical diagnostics by proving that high accuracy does not require prohibitive computational costs or fragility.

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Atlas 2 -- Foundation models for clinical deployment

arXivJuly 28, 20262 min read

arXiv:2601.05148v2 Announce Type: replace-cross 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 Charit\'e - Universit\"atsmedizin Berlin, LMU Munich, and Mayo Clinic.

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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2601.05148 [cs.CV]

(or arXiv:2601.05148v2 [cs.CV] for this version)

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