ClinGRAD: Clinically-Guided Genomics and Radiomics Interpretable GNN for Dementia Diagnosis
Hassan, Salma Salem, Mostafa Papineni, Vijay Ram Kumar Elsayed, Ayman Yaqub, Mohammad
Hassan, Salma
Salem, Mostafa
Papineni, Vijay Ram Kumar
Elsayed, Ayman
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Alzheimer’s Disease (AD) remains a major diagnostic challenge due to the complex interplay of genomic, radiomic, and structural factors in disease progression. While deep learning methods can classify AD, current approaches fail to effectively combine multimodal data with clinical knowledge, compromising both accuracy and interpretability. We present ClinGRAD, a clinically-guided heterogeneous graph neural network that combines genomic and radiomic data using connections based on diffusion-weighted imaging (DWI) maps and gene co-expression networks. ClinGRAD’s contributions include: (1) a multimodal fusion architecture that integrates validated structural and genetic connectivity patterns for consistent biological feature analysis; (2) a multi-scale graph framework capturing both local brain structure and global genomic pathway relationships; (3) an attention mechanism that provides clinically relevant explanations of gene-structure interactions; and (4) pathway-based gene clustering that reveals underlying biological mechanisms and their clinical implications. ClinGRAD outperforms existing models, achieving an accuracy of 93.15%, distinguishing AD from control, mild cognitive impaired, and vascular dementia patients while maintaining biological coherence through its clinical guidance framework. The code is available at https://github.com/BioMedIA-MBZUAI/ClinGRAD
Citation
S. Hassan, M. Salem, V. R. K. Papineni, A. Elsayed, and M. Yaqub, “ClinGRAD: Clinically-Guided Genomics and Radiomics Interpretable GNN for Dementia Diagnosis,” pp. 204–214, 2026, doi: 10.1007/978-3-032-05162-2_20
Source
Medical Image Computing and Computer Assisted Intervention
Conference
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Keywords
Graph Neural Network, Dementia, Neuroimaging, Genomics, Multi-Omics, Interpretability, DWI
Subjects
Source
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher
Springer Nature