SeCoMIL: Semantic Anchor-Based Context-Aware Multiple Instance Learning for Whole Slide Image Classification

Context-aware Multiple Instance Learning (MIL) is gaining popularity in Whole Slide Image (WSI) classification. Existing methods typically convert instances in a WSI into one-dimensional sequences and learn the long-range contextual dependencies among instances. However, due to the extremely large size of WSIs and the morphological similarities within tissue structures, the enormous number of redundant instances significantly increases computational overhead in the context learning paradigm. Additionally, the rearrangement of instances into one dimension loses the inherent spatial information involved in image patches, further compromising the classification performance of pathological images. Consequently, efficiently modeling contextual dependencies in WSIs remains a crucial challenge. In this paper, we propose a novel Semantic Anchor-based Context-aware Multiple Instance Learning (SeCoMIL) framework. This framework partitions the WSI into a series of regions and encodes the coordinates of instances within these regions to preserve their spatial relationships. Subsequently, SeCoMIL identifies the most representative instances from each region as semantic anchors. By capturing both the local context around these anchors and the global context across different anchors, the framework efficiently summarizes the critical pathological information of the WSI, enabling precise classification. Extensive experiments on four public datasets (CAMELYON16, CAMELYON17, TCGA-NSCLC, and TCGA-RCC) demonstrate the robustness of our method, with superior performance compared to state-of-the-art methods.