MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion

• URL: http://arxiv.org/abs/2412.12149v2
• Date: Sun, 12 Jan 2025 02:48:52 GMT
• Title: MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion
• Authors: Manman Yuan, Weiming Jia, Xiong Luo, Jiazhen Ye, Peican Zhu, Junlin Li,
• Abstract summary: A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion is presented. Our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest. We structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix.
• Score: 4.526574526136158
• License:
• Abstract: The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and optimize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://github.com/Jia-Weiming/MHSA.

Related papers

• CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement [24.511384674989223]
  Heart rate (HR) estimation via remote photoplethys (rPl) offers a non-invasive solution for health monitoring. Traditional single-modality approaches (RGB or Radio Frequency (RF)) face challenges in balancing robustness and accuracy due to lighting variations, motion artifacts, and skin tone bias. We propose CardiacMamba, a multimodal RGB-RF fusion framework that leverages the complementary strengths of both modalities.
  arXiv  Detail & Related papers  (2025-02-19T11:00:34Z)
• Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting [16.782154479264126]
  Predicting backbone-temporal traffic flow presents challenges due to complex interactions between temporal factors. Existing approaches address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce Sanonymous-Temporal Unitized Unitized Cell (ASTUC), a unified framework designed to capture both spatial and temporal dependencies.
  arXiv  Detail & Related papers  (2024-11-14T07:34:31Z)
• ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition [5.86850933017833]
  ARN-LSTM architecture is designed to address the challenge of simultaneously capturing spatial motion and temporal dynamics in action sequences. Our proposed model integrates joint, motion, and temporal information through a multi-stream fusion architecture.
  arXiv  Detail & Related papers  (2024-11-04T03:29:51Z)
• Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning [50.74383395813782]
  We propose a novel Frequency and Spatial Mutual Learning Network (FSMNet) to explore global dependencies across different modalities. The proposed FSMNet achieves state-of-the-art performance for the Multi-Contrast MR Reconstruction task with different acceleration factors.
  arXiv  Detail & Related papers  (2024-09-21T12:02:47Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• Coupled Attention Networks for Multivariate Time Series Anomaly Detection [10.620044922371177]
  We propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module.
  arXiv  Detail & Related papers  (2023-06-12T13:42:56Z)
• PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search [56.02761592710612]
  We propose a novel attention-aware relation mixer (ARM) for module person search. Our ARM module is native and does not rely on fine-grained supervision or topological assumptions. Our PS-ARM achieves state-of-the-art performance on both datasets.
  arXiv  Detail & Related papers  (2022-10-07T10:04:12Z)
• Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [79.97180849505294]
  We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI. Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
  arXiv  Detail & Related papers  (2020-07-10T08:08:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.