A multi-modal graph-based framework for Alzheimer’s disease detection
Abstract We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer’s disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes $$n_i$$ , and deep learning (DL) models are rep...
Saved in:
| Main Authors: | Najmeh Mashhadi, Razvan Marinescu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05966-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MERGE: A Modal Equilibrium Relational Graph Framework for Multi-Modal Knowledge Graph Completion
by: Yuying Shang, et al.
Published: (2024-11-01) -
Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism
by: JIANG Di, et al.
Published: (2025-06-01) -
Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation
by: Binbin Fu, et al.
Published: (2025-06-01) -
Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease
by: Yujian Liu, et al.
Published: (2025-06-01) -
Tracing truth: dynamic temporal networks for multi-modal fake news detection
by: Jiaen Hu, et al.
Published: (2025-07-01)