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[2602.15740] MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis
Summary
The paper presents the MRC-GAT, a novel Meta-Relational Copula-Based Graph Attention Network designed for accurate and interpretable Alzheimer's disease diagnosis using multimodal data.
Why It Matters
This research addresses the critical need for early and precise Alzheimer's diagnosis, leveraging advanced machine learning techniques to enhance diagnostic accuracy and interpretability. By overcoming limitations of existing models, it offers a promising approach to improve clinical decision-making in Alzheimer's care.
Key Takeaways
- MRC-GAT integrates copula-based transformation and relational attention for multimodal feature alignment.
- Achieved state-of-the-art diagnostic accuracies of 96.87% and 92.31% on TADPOLE and NACC datasets, respectively.
- The model enhances interpretability, crucial for clinical applications in Alzheimer's diagnosis.
- Addresses limitations of fixed structural designs in existing graph-based diagnostic models.
- Demonstrates the potential of episodic meta-learning in improving model flexibility and generalization.
Computer Science > Machine Learning arXiv:2602.15740 (cs) [Submitted on 17 Feb 2026] Title:MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis Authors:Fatemeh Khalvandi, Saadat Izadi, Abdolah Chalechale View a PDF of the paper titled MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis, by Fatemeh Khalvandi and 2 other authors View PDF Abstract:Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management. Given the paramount importance of early diagnosis, recent studies have increasingly focused on computer-aided diagnostic models to enhance precision and reliability. However, most graph-based approaches still rely on fixed structural designs, which restrict their flexibility and limit generalization across heterogeneous patient data. To overcome these limitations, the Meta-Relational Copula-Based Graph Attention Network (MRC-GAT) is proposed as an efficient multimodal model for AD classification tasks. The proposed architecture, copula-based similarity alignment, relational attention, and node fusion are integrated as the core components of episodic meta-learning, such that the multimodal features, including risk factors (RF), Cognitive test scores, and MRI attributes, are first aligned via a copula-based transformation in a common statistical sp...