Multiple instance learning (MIL) has shown significant promise in histopathology whole slide image (WSI) analysis for cancer diagnosis and prognosis. However, the inherent spatial heterogeneity of WSIs presents critical challenges, as morphologically similar tissue types are often dispersed across distant anatomical regions. Conventional MIL methods struggle to model these scattered tissue distributions and capture cross-regional spatial interactions effectively. To address these limitations, we propose a novel Multiple instance learning framework with Context-Aware Clustering (MiCo), designed to enhance cross-regional intra-tissue correlations and strengthen inter-tissue semantic associations in WSIs. MiCo begins by clustering instances to distill discriminative morphological patterns, with cluster centroids serving as semantic anchors. To enhance cross-regional intra-tissue correlations, MiCo employs a Cluster Route module, which dynamically links instances of the same tissue type across distant regions via feature similarity. These semantic anchors act as contextual hubs, propagating semantic relationships to refine instance-level representations. To eliminate semantic fragmentation and strengthen inter-tissue semantic associations, MiCo integrates a Cluster Reducer module, which consolidates redundant anchors while enhancing information exchange between distinct semantic groups. Extensive experiments on two challenging tasks across nine large-scale public cancer datasets demonstrate the effectiveness of MiCo, showcasing its superiority over state-of-the-art methods. The code is available at https://github.com/junjianli106/MiCo.

翻译：多示例学习（MIL）在用于癌症诊断和预后的组织病理学全切片图像（WSI）分析中展现出巨大潜力。然而，WSI固有的空间异质性带来了关键挑战，因为形态学相似的组织类型常常分散在相距较远的解剖区域。传统的MIL方法难以有效建模这些分散的组织分布并捕获跨区域的空间交互作用。为了解决这些局限性，我们提出了一种新颖的、带有上下文感知聚类的多示例学习框架（MiCo），旨在增强WSI中跨区域的同组织内相关性并强化不同组织间的语义关联。MiCo首先通过聚类实例来提炼具有判别性的形态模式，其中聚类中心点作为语义锚点。为了增强跨区域的同组织内相关性，MiCo采用了一个聚类路由模块，该模块通过特征相似性动态连接远距离区域中的相同组织类型的实例。这些语义锚点充当上下文枢纽，传播语义关系以精炼实例级表示。为了消除语义碎片化并强化不同组织间的语义关联，MiCo集成了一个聚类约简模块，该模块在增强不同语义组之间信息交换的同时，合并冗余的锚点。在九个大规模公开癌症数据集上针对两项具有挑战性的任务进行的广泛实验证明了MiCo的有效性，并展示了其相对于现有最先进方法的优越性。代码可在 https://github.com/junjianli106/MiCo 获取。