arXiv 2308.154742023-08-29cs.CVcs.AIq-bio.TO

通用自监督计算病理学模型

A General-Purpose Self-Supervised Model for Computational Pathology

Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛-麻省理工博德研究所）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Massachusetts Institute of Technology（麻省理工学院）；Harvard John A. Paulson School of Engineering And Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Harvard University（哈佛大学）

Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Wil… 展开作者

Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, Faisal Mahmood

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AI总结：

本文提出通用自监督病理模型UNI，基于超1亿组织块预训练，在33项临床任务上超越现有模型，支持分辨率无关分类、少样本分类及108种癌症亚型泛化，推动计算病理学表示学习。

摘要：

组织表型分析是计算病理学（CPath）中的一项基础任务，旨在对解剖病理学中的组织病理学生物标志物进行客观表征。然而，全切片成像（WSI）带来了复杂的计算机视觉问题，其中WSI的大尺度图像分辨率以及形态表型的巨大多样性阻碍了大规模数据标注。当前的努力提出使用预训练图像编码器，要么从自然图像数据集进行迁移学习，要么在公开可用的组织病理学数据集上进行自监督预训练，但尚未在多种组织类型的大规模范围内得到广泛开发和评估。我们引入了UNI，一种通用的病理学自监督模型，使用来自超过100,000张诊断性苏木精-伊红（H&E）染色WSI的超过1亿个组织块进行预训练，覆盖20种主要组织类型，并在33项具有不同诊断难度的代表性CPath临床任务上进行了评估。除了优于先前的最先进模型外，我们还展示了CPath中的新建模能力，如分辨率无关的组织分类、使用少样本类原型的切片分类，以及在OncoTree代码分类系统中对多达108种癌症类型进行分类的疾病亚型泛化。UNI在CPath中推进了大规模无监督表示学习，无论是在预训练数据还是下游评估方面，使得数据高效的AI模型能够泛化并迁移到解剖病理学中一系列诊断挑战性任务和临床工作流程。

英文摘要：

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining 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 over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. 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 code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.

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