ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2022
Sparse Interpretation of Graph Convolutional Networks for Multi-Modal Diagnosis of Alzheimer's Disease.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.Nature communications · 2026Review
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications.Health data science · 2025Review
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- A brain structure learning-guided multi-view graph representation learning for brain network analysis.Quantitative imaging in medicine and surgery · 2024Article
- A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering.Diagnostics (Basel, Switzerland) · 2023Article
- Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer's Disease Classification.Brain sciences · 2023Article
- Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The interconnected quality of brain regions in neurological disease has immense importance for the development of biomarkers and diagnostics. While Graph Convolutional Network (GCN) methods are fundamentally compatible with discovering the connected role of brain regions in disease, current methods apply limited consideration for node features and their connectivity in brain network analysis. In this paper, we propose a sparse interpretable GCN framework (SGCN) for the identification and classification of Alzheimer's disease (AD) using brain imaging data with multiple modalities. SGCN applies an attention mechanism with sparsity to identify the most discriminative subgraph structure and important node features for the detection of AD. The model learns the sparse importance probabilities for each node feature and edge with entropy,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.