Multimodal Hyperbolic Graph Learning for Alzheimer's Disease Detection
Xie, C.; Zhou, W.; Peng, C.; Hoshyar, A. N.; Xu, C.; Naseem, U.; Xia, F.
Show abstract
Multimodal graph learning techniques have demonstrated significant potential in modeling brain networks for Alzheimers disease (AD) detection. However, most existing methods rely on Euclidean space representations and overlook the scale-free and small-world properties of brain networks, which are characterized by power-law distributions and dense local clustering of nodes. This oversight results in distortions when representing these complex structures. To address this issue, we propose a novel multimodal Poincare Frechet mean graph convolutional network (MochaGCN) for AD detection. MochaGCN leverages the exponential growth characteristics of hyperbolic space to capture the scale-free and small-world properties of multimodal brain networks. Specifically, we combine hyperbolic graph convolution and Poincare Frechet mean to extract features from multimodal brain networks, enhancing their rep-resentations in hyperbolic space. Our approach constructs multimodal brain networks by integrating information from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. Experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art techniques.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding 92%
- SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG 91%
- A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset 91%
Similar papers in this journal
- Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained Lasso 94%
- Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects 94%
- Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet 93%
Similar papers in this journal
- Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals 95%
- SN-FPN: Self-attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation 94%
- End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligence 93%
Similar papers in this journal
Similar papers in this journal
- Generation of realistic synthetic data using multimodal neural ordinary differential equations 90%
- Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults 90%
- Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.