Atlas 2系列模型提升病理影像诊断性能与效率，助力临床落地。
Atlas 2 -- Foundation models for clinical deployment
- 基于550万张病理切片训练，覆盖三所顶级医院数据。
- 在80个公开基准上实现顶尖预测性能与鲁棒性。
- 兼顾高精度与低资源消耗，适合真实医疗场景部署。
病理基础模型显著拓展了计算病理学的潜力，但其在性能、鲁棒性和计算需求之间的权衡仍限制了临床应用。本文提出Atlas 2、Atlas 2-B和Atlas 2-S三个病理视觉基础模型，通过在迄今最大的病理基础模型数据集（包含550万张组织病理全切片图像）上训练，在80个公共基准上的综合评估中实现了最先进的预测性能、鲁棒性和资源效率。数据来自Charité - Universitätsmedizin Berlin、LMU Munich和Mayo Clinic三家医学机构。
原文摘要 · Abstract (English)
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.
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