Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine. However, there is a mismatch between most clinical analysis, which is defined at the level of one or more whole slide images, and foundation models to date, which process the thousands of image tiles contained in a whole slide image separately. The requirement to train a network to aggregate information across a large number of tiles in multiple whole slide images limits these models' impact. In this work, we present a slide-level foundation model for H&E-stained histopathology, PRISM, that builds on Virchow tile embeddings and leverages clinical report text for pre-training. Using the tile embeddings, PRISM produces slide-level embeddings with the ability to generate clinical reports, resulting in several modes of use. Using text prompts, PRISM achieves zero-shot cancer detection and sub-typing performance approaching and surpassing that of a supervised aggregator model. Using the slide embeddings with linear classifiers, PRISM surpasses supervised aggregator models. Furthermore, we demonstrate that fine-tuning of the PRISM slide encoder yields label-efficient training for biomarker prediction, a task that typically suffers from low availability of training data; an aggregator initialized with PRISM and trained on as little as 10% of the training data can outperform a supervised baseline that uses all of the data.

翻译：计算病理学中的基础模型有望解锁精准医学中新型临床决策支持系统与模型的开发。然而，大多数临床分析定义于一个或多个全切片图像层级，而现有基础模型却分别处理全切片图像中包含的数千个图像图块，两者之间存在不匹配。训练一个网络以跨多个全切片图像的巨量图块聚合信息的需求制约了这些模型的影响力。本研究提出了一种针对H&E染色组织病理学的全切片级基础模型PRISM，该模型基于Virchow图块嵌入技术，并利用临床报告文本进行预训练。通过图块嵌入，PRISM能够生成具备临床报告能力的全切片级嵌入，从而实现多种使用模式。借助文本提示，PRISM在零样本癌症检测与亚型分类任务中表现出接近甚至超越监督聚合模型的性能。通过将全切片嵌入与线性分类器结合，PRISM超越了监督聚合模型。此外，实验表明，对PRISM全切片编码器进行微调，可为通常因训练数据稀缺而受限的生物标志物预测任务实现标签高效训练——使用仅10%训练数据初始化并训练的PRISM聚合器，其表现可超越使用全部数据的监督基线模型。