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 Charit\'e - Universt\"atsmedizin 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.

本文研究了数字病理学中的一个重要问题，即如何有效利用基础模型。提出的Atlas模型基于RudolfV方法，在120万张来自梅奥诊所和查理特医院的全病理切片图像上进行训练。研究表明，Atlas在21个公共基准数据集上表现出色，具有显著的性能提升，尽管其参数数量和训练数据集规模并不是最大的。 

Atlas：梅奥诊所、查理特医院和Aignostics的全新病理基础模型