HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease
This addresses the challenge of improving Alzheimer's disease diagnosis for patients and clinicians by enhancing multimodal integration and knowledge incorporation, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of accurately diagnosing Alzheimer's disease by integrating multimodal data and clinical expertise, proposing HoloDx, a framework that outperforms state-of-the-art methods on five datasets with superior diagnostic accuracy and strong generalization.
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.