基于计算机识别乳腺癌淋巴结转移（BCLNM）的全幻镜病理图像（WSIs）可以大大有益于治疗决策和预后分析。除了计算病理学的一般挑战，如超高分辨率、昂贵的细粒度注释等，该任务面临两个特殊困难：（1）对BCLNM病理图像中显著的肿瘤异质性进行建模，（2）识别微小转移瘤，即转移病灶很小的转移性肿瘤。为此，本文提出了一种新的弱监督方法，称为原型多示例学习（PMIL），只利用幻灯片层级的类标签学习预测从WSIs中的BCLNM。PMIL将基于词汇的多示例学习（MIL）范例引入计算病理学中，其特点是利用所谓的原型来建模病理数据和构建WSI特征。PMIL主要由两个创新设计的模块组成，即原型发现模块从训练数据中使用无监督聚类获取原型，以及基于原型的幻灯片嵌入模块通过将组成片段与原型匹配来构建WSI特征。与现有的WSI分类的MIL方法相比，PMIL具有两个显著的优点：（1）在建模BCLNM病理图像中的肿瘤异质性方面更为明确和可解释，（2）在识别微小转移瘤方面具有更高的效果。对Camelyon16公共数据集和我们自己创建的Zbraln数据集进行评估。PMIL在Camelyon16上的AUC为88.2％，在40倍放大系数下的Zbraln为98.4％，其表现始终优于其他比较方法。还将进行全面的分析以进一步揭示所提出方法的有效性和优点。版权所有©2023年。由Elsevier B.V.出版。

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method.Copyright © 2023. Published by Elsevier B.V.