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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.05342 |
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| _version_ | 1866913535894749184 |
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| author | Gao, Wenjing Yang, Yuanyuan Wei, Jianrui Yin, Xuntao Di, Xinhan |
| author_facet | Gao, Wenjing Yang, Yuanyuan Wei, Jianrui Yin, Xuntao Di, Xinhan |
| contents | The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning framework that can capture more information in limited data and insufficient supervision. To address these issues at some extend, we propose a multi-stage graph learning framework which incorporates 1) pretrain stage : self-supervised graph learning on insufficient supervision of the fmri data 2) fine-tune stage : supervised graph learning for brain disorder diagnosis. Experiment results on three datasets, Autism Brain Imaging Data Exchange ABIDE I, ABIDE II and ADHD with AAL1,demonstrating the superiority and generalizability of the proposed framework compared to the state of art of models.(ranging from 0.7330 to 0.9321,0.7209 to 0.9021,0.6338 to 0.6699) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05342 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders Gao, Wenjing Yang, Yuanyuan Wei, Jianrui Yin, Xuntao Di, Xinhan Neurons and Cognition Computer Vision and Pattern Recognition Image and Video Processing The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning framework that can capture more information in limited data and insufficient supervision. To address these issues at some extend, we propose a multi-stage graph learning framework which incorporates 1) pretrain stage : self-supervised graph learning on insufficient supervision of the fmri data 2) fine-tune stage : supervised graph learning for brain disorder diagnosis. Experiment results on three datasets, Autism Brain Imaging Data Exchange ABIDE I, ABIDE II and ADHD with AAL1,demonstrating the superiority and generalizability of the proposed framework compared to the state of art of models.(ranging from 0.7330 to 0.9321,0.7209 to 0.9021,0.6338 to 0.6699) |
| title | Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2410.05342 |