Cargando…
Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis
Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative diseases, such as autism spectrum disorder (ASD). How...
Autores principales: | Zhao, Feng, Li, Na, Pan, Hongxin, Chen, Xiaobo, Li, Yuan, Zhang, Haicheng, Mao, Ning, Cheng, Dapeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334869/ https://www.ncbi.nlm.nih.gov/pubmed/35911592 http://dx.doi.org/10.3389/fnhum.2022.918969 |
Ejemplares similares
-
A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder
por: Shao, Lizhen, et al.
Publicado: (2023) -
Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism
por: Peng, Wei, et al.
Publicado: (2023) -
Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion
por: Chen, Chengjun, et al.
Publicado: (2022) -
Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
por: Oluwasanmi, Ariyo, et al.
Publicado: (2023) -
Multi-View Spatial-Temporal Graph Convolutional Networks With Domain Generalization for Sleep Stage Classification
por: Jia, Ziyu, et al.
Publicado: (2021)