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期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02857-3

摘要内容如下：

组织图像的定量评估对于计算病理学（CPATH）任务至关重要，需要从全切片图像（WSIS）中客观表征组织病理学实体。WSIS的高分辨率和形态特征的可变性带来了巨大的挑战，使高性能应用的大规模数据标注变得复杂。为了应对这一挑战，目前的努力已经提出通过从自然图像数据集的迁移学习或在公开可用的组织病理学数据集上的自我监督学习来使用预训练的图像编码器，但尚未在不同组织类型中大规模地广泛开发和评估。我们介绍了UNI，这是一种通用的病理学自我监督模型，使用来自20种主要组织类型的100，000多个诊断H&E染色的WSI（>77TB的数据）的1亿多个图像进行预训练。该模型在34个不同诊断难度的代表性CPATH任务上进行了评估。除了优于以前最先进的模型外，我们还展示了CPATH中的新建模功能，如分辨率不可知的组织分类、使用少量类别原型的切片分类，以及在OncoTree分类系统中对多达108种癌症类型进行分类的疾病亚型泛化。在预训练数据和下游评估方面，UNI在CPATH中大规模推进无监督表征学习，使数据高效的人工智能模型能够推广和转移到解剖病理学中广泛的诊断挑战性任务和临床工作流程。

英文原文如下：

Abstracts

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

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