HyperGALE: ASD Classification via Hypergraph Gated Attention with Learnable Hyperedges
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arXiv
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| Format: | Preprint |
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2024
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| author | Arora, Mehul Jain, Chirag Shantilal Baru, Lalith Bharadwaj Dadi, Kamalaker Surampudi, Bapi Raju |
| author_facet | Arora, Mehul Jain, Chirag Shantilal Baru, Lalith Bharadwaj Dadi, Kamalaker Surampudi, Bapi Raju |
| contents | Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by varied social cognitive challenges and repetitive behavioral patterns. Identifying reliable brain imaging-based biomarkers for ASD has been a persistent challenge due to the spectrum's diverse symptomatology. Existing baselines in the field have made significant strides in this direction, yet there remains room for improvement in both performance and interpretability. We propose \emph{HyperGALE}, which builds upon the hypergraph by incorporating learned hyperedges and gated attention mechanisms. This approach has led to substantial improvements in the model's ability to interpret complex brain graph data, offering deeper insights into ASD biomarker characterization. Evaluated on the extensive ABIDE II dataset, \emph{HyperGALE} not only improves interpretability but also demonstrates statistically significant enhancements in key performance metrics compared to both previous baselines and the foundational hypergraph model. The advancement \emph{HyperGALE} brings to ASD research highlights the potential of sophisticated graph-based techniques in neurodevelopmental studies. The source code and implementation instructions are available at GitHub:https://github.com/mehular0ra/HyperGALE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14484 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HyperGALE: ASD Classification via Hypergraph Gated Attention with Learnable Hyperedges Arora, Mehul Jain, Chirag Shantilal Baru, Lalith Bharadwaj Dadi, Kamalaker Surampudi, Bapi Raju Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Neural and Evolutionary Computing Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by varied social cognitive challenges and repetitive behavioral patterns. Identifying reliable brain imaging-based biomarkers for ASD has been a persistent challenge due to the spectrum's diverse symptomatology. Existing baselines in the field have made significant strides in this direction, yet there remains room for improvement in both performance and interpretability. We propose \emph{HyperGALE}, which builds upon the hypergraph by incorporating learned hyperedges and gated attention mechanisms. This approach has led to substantial improvements in the model's ability to interpret complex brain graph data, offering deeper insights into ASD biomarker characterization. Evaluated on the extensive ABIDE II dataset, \emph{HyperGALE} not only improves interpretability but also demonstrates statistically significant enhancements in key performance metrics compared to both previous baselines and the foundational hypergraph model. The advancement \emph{HyperGALE} brings to ASD research highlights the potential of sophisticated graph-based techniques in neurodevelopmental studies. The source code and implementation instructions are available at GitHub:https://github.com/mehular0ra/HyperGALE. |
| title | HyperGALE: ASD Classification via Hypergraph Gated Attention with Learnable Hyperedges |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2403.14484 |