Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis and clinical practice. Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI. In this paper, we aim to learn an optimal patch-level feature space by integrating prototype learning with MIL. To this end, we develop a Trainable Prototype enhanced deep MIL (TPMIL) framework for weakly supervised WSI classification. In contrast to the conventional methods which rely on a certain number of selected patches for feature space refinement, we softly cluster all the instances by allocating them to their corresponding prototypes. Additionally, our method is able to reveal the correlations between different tumor subtypes through distances between corresponding trained prototypes. More importantly, TPMIL also enables to provide a more accurate interpretability based on the distance of the instances from the trained prototypes which serves as an alternative to the conventional attention score-based interpretability. We test our method on two WSI datasets and it achieves a new SOTA. GitHub repository: https://github.com/LitaoYang-Jet/TPMIL

翻译：基于全切片图像（WSI）的数字病理学在癌症诊断和临床实践中发挥着关键作用。由于WSI的高分辨率和缺乏补丁级标注，WSI分类通常被建模为弱监督问题，依赖于基于WSI补丁的多示例学习（MIL）。本文旨在通过将原型学习与MIL相结合，学习最优的补丁级特征空间。为此，我们提出了一种可训练原型增强的深度MIL（TPMIL）框架，用于弱监督WSI分类。与依赖固定数量选定补丁进行特征空间优化的传统方法不同，我们通过将所有实例分配到对应原型来实现软聚类。此外，该方法能通过训练后原型间的距离揭示不同肿瘤亚型之间的关联性。更重要的是，TPMIL还能基于实例与训练后原型的距离提供更精确的可解释性，作为传统基于注意力分数的可解释性的替代方案。我们在两个WSI数据集上测试了该方法，取得了新的最优性能。GitHub仓库：https://github.com/LitaoYang-Jet/TPMIL