融合医学知识与多模态数据，提升阿尔茨海默病诊断准确率
HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease
- 通过动态知识注入模块融合临床知识与大模型见解
- 在五个数据集上实现最优诊断准确率，泛化能力更强
- 适合需要可解释性诊断系统的医疗AI研究者
阿尔茨海默病（AD）的精准诊断需有效整合多模态数据与临床知识。现有方法常难以充分利用多模态信息，且缺乏结构化机制来融入动态领域知识。为此，我们提出HoloDx——一种知识驱动与数据驱动相结合的框架，通过将领域知识与多模态临床数据对齐，提升AD诊断性能。HoloDx包含知识注入模块，采用知识感知门控交叉注意力，实现从大语言模型（LLMs）和临床专家处动态引入领域知识；同时引入记忆注入模块，结合原型记忆注意力机制，保留并检索个体特异性信息，确保决策一致性。联合使用这两项机制后，HoloDx显著增强可解释性、提升鲁棒性，并有效对齐先验知识与当前患者数据。在五个AD数据集上的评估表明，该方法优于现有最先进模型，实现更高的诊断准确率及强跨队列泛化能力。源代码将在论文录用后公开。
原文摘要 · Abstract (English)
Accurate diagnosis of Alzheimer's disease (AD) requires effectively integrating multimodal data and clinical expertise. However, existing methods often struggle to fully utilize multimodal information and lack structured mechanisms to incorporate dynamic domain knowledge. To address these limitations, we propose HoloDx, a knowledge- and data-driven framework that enhances AD diagnosis by aligning domain knowledge with multimodal clinical data. HoloDx incorporates a knowledge injection module with a knowledge-aware gated cross-attention, allowing the model to dynamically integrate domain-specific insights from both large language models (LLMs) and clinical expertise. Also, a memory injection module with a designed prototypical memory attention enables the model to retain and retrieve subject-specific information, ensuring consistency in decision-making. By jointly leveraging these mechanisms, HoloDx enhances interpretability, improves robustness, and effectively aligns prior knowledge with current subject data. Evaluations on five AD datasets demonstrate that HoloDx outperforms state-of-the-art methods, achieving superior diagnostic accuracy and strong generalization across diverse cohorts. The source code will be released upon publication acceptance.
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