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.

从全幻灯片病理图像 (WSI) 中计算机化识别乳腺癌淋巴结转移 (BCLNM) 可以在很大程度上有利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、非常昂贵的细粒度注释等，这项任务的两个特殊困难在于（1）对 BCLNM 病理图像中显着的肿瘤间异质性进行建模，以及（2 ) 识别微转移，即具有微小病灶的转移性肿瘤。为此，本文提出了一种新的弱监督方法，称为原型多实例学习 (PMIL)，以学习从仅具有幻灯片级别标签的 WSI 预测 BCLNM。PMIL 将成熟的基于词汇的多实例学习 (MIL) 范式引入计算病理学，其特点是利用所谓的原型对病理数据进行建模并构建 WSI 特征。PMIL主要由两个创新设计的模块组成，即通过无监督聚类从训练数据中获取原型的原型发现模块，以及通过将本构补丁与原型匹配来构建WSI特征的基于原型的幻灯片嵌入模块。相对于现有的用于 WSI 分类的 MIL 方法，PMIL 具有两个实质性优点：（1）在 BCLNM 病理图像中对肿瘤间异质性进行建模时更加明确和可解释，以及（2）在识别微转移方面更加有效。评估是在两个数据集上进行的，即公开的 Camelyon16 数据集和我们自己创建的 Zbraln 数据集。PMIL 在 Camelyon16 和 98 上实现了 88.2% 的 AUC。Zbraln 为 4%（放大倍数为 40 倍），其性能始终优于其他比较方法。还将进行综合分析，以进一步揭示所提出方法的有效性和优点。