Atlas用百万张病理切片训练，性能超越多数大模型。
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
- 基于RudolfV架构，用120万张病理切片训练
- 在21个公开数据集上达到顶尖水平
- 小体积却强性能，适合医疗影像研究
数字病理学的最新进展表明，基础模型在多种应用中表现出色。本文介绍Atlas，一种基于RudolfV架构的新视觉基础模型。该模型在来自梅奥诊所与柏林夏里特大学医学院的120万张组织病理全切片图像数据集上进行训练。全面评估显示，尽管Atlas在参数量和训练数据规模上并非最大，但在21个公开基准数据集上仍达到领先性能。
原文摘要 · Abstract (English)
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.
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