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

翻译：病理学基础模型极大地推进了计算病理学的可能性——然而，在性能、鲁棒性和计算需求方面存在的权衡，限制了其在临床的部署。在本报告中，我们介绍了Atlas 2、Atlas 2-B和Atlas 2-S这三个病理学视觉基础模型，它们通过在涵盖八十个公共基准的全面评估中展现出预测性能、鲁棒性和资源效率方面的最先进水平，从而弥补了这些不足。我们的模型基于迄今为止最大的病理学基础模型数据集进行训练，该数据集包含550万张组织病理学全切片图像，收集自柏林夏里特医学院、慕尼黑大学和梅奥诊所这三家医疗机构。