Mamba-HMIL: Hierarchical Multiple Instance Learning via State Space Model for Whole Slide Image Diagnosis

Mengkang Lu, Tianyi Wang, Qiwei Fu, Qingjie Zeng, ZhaoyangLIU, shu minglei, Yong Xia

Keywords: Whole Slide Images, Hierarchical Multiple Instance Learning, State Space Model.

Abstract: Multiple instance learning (MIL) has been widely employed for gigapixel whole slide image (WSI) classification. Existing MIL methods, however, are found wanting to align with the clinical practice of pathologists, who typically scrutinize WSIs at varied scales and compare the local regions in a global perspective. Given that WSIs usually boast immense dimensions peppered with large regions not pertinent to diagnosis, we propose a novel hierarchical multiple instance learning method based on the state space model, called Mamba-HMIL, for WSI classification. Mamba-HMIL consists of three primary modules to enhance the performance of MIL. First, the hierarchical feature extractor harvests features across diverse scales. Second, for capturing the correlation among patches, the state space model demonstrates robust modeling capabilities. A Mixture of Experts (MoE) module is for stable SSM training. Third, the adaptive selection model strives to reduce redundancies by focusing on disease-positive regions. We evaluate Mamba-HMIL on two WSI subtype datasets (TCGA-NSCLC and TCGA-RCC) and two WSI survival datasets (TCGA-BRCA and TCGA-BLCA). Our results suggest that Mamba-HMIL outperforms existing MIL methods on both WSI tasks. Our code will be made publicly available.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 1404