Computational pathology uses artificial intelligence to enable precision medicine and decision support systems through the analysis of whole slide images. It has the potential to revolutionize the diagnosis and treatment of cancer. However, a major challenge to this objective is that for many specific computational pathology tasks the amount of data is inadequate for development. To address this challenge, we created Virchow, a 632 million parameter deep neural network foundation model for computational pathology. Using self-supervised learning, Virchow is trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue groups, which is orders of magnitude more data than previous works. When evaluated on downstream tasks including tile-level pan-cancer detection and subtyping and slide-level biomarker prediction, Virchow outperforms state-of-the-art systems both on internal datasets drawn from the same population as the pretraining data as well as external public datasets. Virchow achieves 93% balanced accuracy for pancancer tile classification, and AUCs of 0.983 for colon microsatellite instability status prediction and 0.967 for breast CDH1 status prediction. The gains in performance highlight the importance of pretraining on massive pathology image datasets, suggesting pretraining on even larger datasets could continue improving performance for many high-impact applications where limited amounts of training data are available, such as drug outcome prediction.

翻译：计算病理学通过人工智能分析全切片图像，旨在实现精准医学和决策支持系统。该技术有望彻底改变癌症的诊断与治疗。然而，实现这一目标的主要挑战在于，许多特定的计算病理学任务缺乏足够的数据支持其开发。为解决这一问题，我们创建了 Virchow——一个拥有6.32亿参数的深度神经网络基础模型，专用于计算病理学。该模型通过自监督学习，基于来自不同组织群的150万张苏木精-伊红染色全切片图像进行训练，训练数据量较此前研究高出数个数量级。在下游任务评估中，包括组织切片级别的泛癌检测与亚型分类，以及全切片级别的生物标志物预测，Virchow不仅在源自同一预训练数据总体的内部数据集中，更在外部公开数据集上均超越了现有最优系统。该模型在泛癌组织切片分类中实现了93%的平衡准确率，在结肠癌微卫星不稳定性状态预测和乳腺癌CDH1状态预测中分别取得了0.983和0.967的AUC值。性能提升凸显了在大规模病理图像数据集上预训练的重要性，表明在药物结局预测等训练数据有限的高影响力应用中，在更大数据集上进行预训练有望持续改善模型表现。