Alzheimer's Disease Classification

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Alzheimer’s disease classification aims to distinguish cognitively healthy people, mild cognitive impairment, and Alzheimer’s disease, and to identify who may progress from one stage to another. Common inputs include MRI and PET scans, EEG, speech or handwriting, and clinical or cognitive measures, analyzed with methods such as convolutional networks, graph learning, transformers, and multimodal fusion. Researchers also study interpretable predictions, calibrated uncertainty, longitudinal models, and systems that can work when some tests are missing or costly. A central challenge is whether strong results transfer across cohorts, populations, and clinical settings, since data leakage, inconsistent evaluation, and changes in scanners or speech collection can undermine apparent accuracy.

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93 papers10 papers in the last four weeks, down 9% on the four weeks before. 0.1% of all new papers.

MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis
Feb 17, 2026Fatemeh Khalvandi, Saadat Izadi, Abdolah ChalechaleAlzheimer's Disease Classification Graph Attention Networks
Computer Engineering Department, Razi University, Kermanshah, Iran
Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation
Nov 8, 2025Changqing Gong, Huafeng Qin, Mounim A. El-YacoubiAlzheimer's Disease VLM Adaptation
Telecom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France · School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, China
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Oct 13, 2025Jun-En Ding, Anna Zilverstand, Shihao Yang +2Alzheimer's Disease Electroencephalography
Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA · Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA · Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University, Taipei City, Taiwan

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MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

Fatemeh Khalvandi, Saadat Izadi, Abdolah Chalechale

Computer Engineering Department, Razi University, Kermanshah, Iran

Alzheimer's Disease Classification Graph Attention Networks Multimodal Graph Learning Multimodal Model Interpretability Cross-Modal Alignment

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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 space and then combined by a multi-relational attention mechanism. According to evaluations performed on the TADPOLE and NACC datasets, the MRC-GAT model achieved accuracies of 96.87% and 92.31%, respectively, demonstrating state-of-the-art performance compared to existing diagnostic models. Finally, the proposed model confirms the robustness and applicability of the proposed method by providing interpretability at various stages of disease diagnosis.

Latest papers

MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation

Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks