Summary of Mhsa: a Multi-scale Hypergraph Network For Mild Cognitive Impairment Detection Via Synchronous and Attentive Fusion, by Manman Yuan et al.
MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion
by Manman Yuan, Weiming Jia, Xiong Luo, Jiazhen Ye, Peican Zhu, Junlin Li
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework, MHSA (Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion), is proposed to tackle the challenge of hypergraph modeling with synchronization between brain regions. This approach employs Phase-Locking Value (PLV) to calculate phase synchronization relationships in the spectrum domain of Regions of Interest (ROIs). A multi-scale feature fusion mechanism integrates dynamic connectivity features from functional magnetic resonance imaging (fMRI) in both temporal and spectrum domains. The framework is evaluated on a real-world dataset, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect mild cognitive impairment (MCI) uses brain networks to help patients get the right treatment at the right time. This method looks at how different parts of the brain work together and uses something called phase-locking value to understand how they are connected. It then combines this information with other data from brain scans to create a better picture of what’s happening in the brain. The results show that this approach is helpful for detecting MCI. |
