KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis

[2608.14757] KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2608.14757 (eess) [Submitted on 14 Aug 2026] Title: KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis Authors: Qixiang Zhang , Yi Li , Tianqi Xiang , Haonan Wang , Mengjiao Wei , Bo Xu , Xiaomeng Li View a PDF of the paper titled KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis, by Qixiang Zhang and 6 other authors View PDF HTML (experimental) Abstract: Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated into a slide representation, a process we term slide encoding dynamics. Recently, selective state-space models (SSM) have emerged as promising MIL architectures due to their long-sequence modeling capability and linear complexity. However, existing SSM-based MIL methods rely solely on visual features during MIL. Meanwhile, in large-scale WSIs, where sparse diagnostically decisive regions are surrounded by abundant irrelevant information, such purely vision-driven selective dynamics can misallocate state updates and readouts, causing the evolving SSM state to accumulate task-irrelevant evidence and dilute critical diagnostic cues over long scan trajectories. In this work, we propose the Knowledge-Aware Hidden-State Modulation architecture (KHiM-Mamba), which innovatively regulates Mamba's core selective state-space mechanism with explicit knowledge priors, steering slide encoding dynamics toward diagnostically meaningful evidence accumulation. Specifically, we redesign the original SSM layer to perform knowledge modulation operations during the evolution of hidden states, thereby guiding what visual evidence is accumulated and retrieved from the hidden state at each encoding step. Furthermore, we additionally introduce a local-adaptive vocabulary retrieval module that uses large language models to assign each patch fine-grained, tissue-specific semantic descriptions, enabling precise modulation across diverse tasks. Experiments on 11 public benchmarks across 4 tasks show that KHiM-Mamba consistently achieves state-of-the-art performance. Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM) Cite as: arXiv:2608.14757 [eess.IV] (or arXiv:2608.14757v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.14757 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qixiang Zhang [ view email ] [v1] Fri, 14 Aug 2026 07:45:09 UTC (5,826 KB) Full-text links: Access Paper: View a PDF of the paper titled KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis, by Qixiang Zhang and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV eess q-bio q-bio.QM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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