NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification.
Shuaiqi LiuBeibei LiangSiqi WangBing LiLidong PanShui-Hua WangPublished in: IEEE open journal of engineering in medicine and biology (2023)
Goal: The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. Methods: we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification. Results: Compared with other models, the NF-GAT has significant advantages in terms of classification results. Conclusions: NF-GAT can be effectively used for ASD classification.
Keyphrases
- autism spectrum disorder
- functional connectivity
- deep learning
- convolutional neural network
- machine learning
- lymph node
- resting state
- signaling pathway
- working memory
- attention deficit hyperactivity disorder
- lps induced
- intellectual disability
- magnetic resonance imaging
- pi k akt
- neural network
- nuclear factor
- oxidative stress
- artificial intelligence
- sentinel lymph node
- healthcare
- inflammatory response
- computed tomography
- squamous cell carcinoma
- cell proliferation
- early stage