Virchow：百万级数字病理学基础模型
Virchow: A Million-Slide Digital Pathology Foundation Model
AI总结：
提出Virchow，一个基于DINOv2自监督学习、在150万张H&E染色全切片图像上训练的6.32亿参数视觉Transformer模型，用于计算病理学，在泛癌检测和生物标志物预测任务上达到最先进性能。
摘要：
通过分析病理图像实现精准医疗和决策支持系统的人工智能应用，有潜力彻底改变癌症的诊断和治疗。这类应用将依赖于模型捕捉病理图像中观察到的多样化模式的能力。为应对这一挑战，我们提出了Virchow，一个用于计算病理学的基础模型。利用DINOv2算法支持的自监督学习，Virchow是一个拥有6.32亿参数的视觉Transformer模型，在来自不同组织和标本类型的150万张苏木精-伊红染色全切片图像上训练，数据量比以往工作高出数个数量级。Virchow模型使得开发一个泛癌检测系统成为可能，该系统在17种不同癌症类型上的整体标本级AUC达到0.949，同时在7种罕见癌症类型上达到0.937的AUC。Virchow模型在内部和外部图像块级基准测试以及切片级生物标志物预测任务上均达到了最先进水平。性能的提升凸显了在大型病理图像数据集上训练的重要性，表明扩展数据和网络架构可以提高许多高影响计算病理学应用的准确性，尤其是在训练数据有限的情况下。
英文摘要：
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer. Such applications will depend on models' abilities to capture the diverse patterns observed in pathology images. To address this challenge, we present Virchow, a foundation model for computational pathology. Using self-supervised learning empowered by the DINOv2 algorithm, Virchow is a vision transformer model with 632 million parameters trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue and specimen types, which is orders of magnitude more data than previous works. The Virchow model enables the development of a pan-cancer detection system with 0.949 overall specimen-level AUC across 17 different cancer types, while also achieving 0.937 AUC on 7 rare cancer types. The Virchow model sets the state-of-the-art on the internal and external image tile level benchmarks and slide level biomarker prediction tasks. The gains in performance highlight the importance of training on massive pathology image datasets, suggesting scaling up the data and network architecture can improve the accuracy for many high-impact computational pathology applications where limited amounts of training data are available.
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