Multi-Modal Graph Construction and Classification for ASD
What it is
A deep learning framework for diagnosing Autism Spectrum Disorder (ASD) using functional MRI (fMRI) data and phenotypic attributes. It employs a dual-stream Graph Convolutional Network (GCN) architecture that integrates DeepWalk embeddings and adaptive edge pruning to model brain connectivity.
The problem it solves
Difficulty in accurately classifying ASD from complex, high-dimensional fMRI data and multi-modal inputs.
What you could use it for
- Automated diagnosis of Autism Spectrum Disorder (ASD) from fMRI scans.
- Modeling complex brain topological and hierarchical relationships.
- Analyzing multi-modal datasets combining imaging data with demographic phenotypes.
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