KHiM-Mamba：通过隐藏状态调制将病理知识注入Mamba以用于 whole slide image（WSI）分析
KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis
- The Hong Kong University of Science and Technology（香港科技大学）
- Chongqing University Cancer Hospital（重庆大学附属肿瘤医院）
- Chongqing University School of Medicine（重庆大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出KHiM-Mamba架构，通过将病理知识注入Mamba的选择性状态空间机制，解决纯视觉驱动的WSI分析问题，在4类任务的11个公共基准上取得最优性能。
AI中文摘要：
全玻片图像（Whole Slide Image, WSI）分析通常被建模为多实例学习（Multiple Instance Learning, MIL），其中实例特征会经过上下文更新并聚合为玻片表示，这一过程我们称为玻片编码动态。近年来，选择性状态空间模型（Selective State-Space Models, SSM）凭借其长序列建模能力和线性复杂度，成为颇具潜力的MIL架构。然而，现有基于SSM的MIL方法在MIL过程中仅依赖视觉特征。同时，在包含大量无关信息、仅存在稀疏诊断决定性区域的大规模WSI中，这种纯视觉驱动的选择性动态会错误分配状态更新与读取，导致演化中的SSM状态在长扫描轨迹上积累与任务无关的证据，稀释关键诊断线索。本研究中，我们提出知识感知隐藏状态调制架构（Knowledge-Aware Hidden-State Modulation architecture, KHiM-Mamba），其通过显式知识先验创新性地调控Mamba的核心选择性状态空间机制，引导玻片编码动态朝向诊断意义证据积累。具体而言，我们重新设计原始SSM层，使其在隐藏状态演化过程中执行知识调制操作，从而在每个编码步骤指导从隐藏状态中积累和检索哪些视觉证据。此外，我们额外引入局部自适应词汇检索模块，该模块利用大型语言模型为每个图像块分配细粒度的、组织特异性的语义描述，实现跨不同任务的精确调制。在4项任务的11个公共基准上进行的实验表明，KHiM-Mamba始终达到了最先进的性能。
英文摘要：
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.
