Knowledge-driven Multiple Instance Learning with Hierarchical Cluster-incorporated Aware Filtering for Larynx Pathological Grading

Pathological grading of laryngeal squamous cell carcinoma (LSCC) based on whole-slide image (WSI) is crucial for the diagnosis, treatment and prognosis. According to pathologists' knowledge, tumor regions are highly associated with grading. However, existing multiple instance learning (MIL) methods tend to overrepresent weakly relevant non-tumor regions and irrelevant background, leading to poor grading performance and interpretability. Motivated by the above problems, we propose an end-to-end knowledge-driven MIL network with hierarchical cluster-incorporated aware filtering, i.e. HCF-MIL. Firstly, we develop the tumor-guiding cluster filtering for feature representation, which awarely filters out irrelevant instance-level information and adaptively assigns learnable weights to tumor and non-tumor instances. Secondly, conventional mean-based and max-based aggregation primarily capture the overall patterns, neglecting the contributions of the most representative individual instances. Therefore, we propose a novel enhanced filtering aggregation learning strategy to strengthen hierarchical tumor-related feature representation. Through end-to-end optimization, HCF-MIL reduces model's entropy value and facilitates better alignment between decision-making process and diagnostic behaviors of pathologists. Experiments on larynx and multicentre datasets show that HCF-MIL significantly improves both pathological grading performance and interpretability, providing a strong foundation for reliable clinical deployment. The dataset and code will be available at https://github.com/Prince-Lee-PathAI/HCF-MIL.