MoMIL: Multi-order enhanced multiple instance learning for computational pathology

Computational pathology (CPath) has significantly advanced the clinical practice of pathology. Despite the progress made, Multiple Instance Learning (MIL), a promising paradigm within CPath, continues to face challenges, especially those related to structural fixation and incomplete information utilization. To address these limitations, we propose a novel MIL framework named Multi-order MIL (MoMIL). Our framework utilizes the SSD model to perform long-sequence modeling on multi-order WSI patches and combines lightweight feature fusion to achieve more comprehensive feature information utilization. This framework supports the fusion of a broader range of features and is highly flexible, allowing for expansion based on specific usage requirements. Additionally, we introduce a sequence transformation method specifically designed for WSIs. This method is not only adaptable to different WSI sizes but also captures additional feature expression, resulting in a more effective exploitation of sequential cues. Extensive experiments demonstrate that MoMIL surpasses state-of-the-art MIL methods, up to 0.027 AUC improvements for cancer sub-typing. We conducted extensive experiments on three downstream tasks with a total of five datasets, achieving improvements in all performance metrics. The code is available at https://github.com/YuqiZhang-Buaa/MoMIL.