• Corpus ID: 239015937

A Heterogeneous Graph Based Framework for Multimodal Neuroimaging Fusion Learning

@article{Shi2021AHG,
  title={A Heterogeneous Graph Based Framework for Multimodal Neuroimaging Fusion Learning},
  author={Gen Shi and Yifan Zhu and Wenjin Liu and Xuesong Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.08465}
}
Here, we present a Heterogeneous Graph neural network for Multimodal neuroimaging fusion learning (HGM). Traditional GNN-based models usually assume the brain network is a homogeneous graph with single type of nodes and edges. However, vast literatures have shown the heterogeneity of the human brain especially between the two hemispheres. Homogeneous brain network is insufficient to model the complicated brain state. Therefore, in this work we firstly model the brain network as heterogeneous… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 75 REFERENCES
Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis
TLDR
An Attention-Diffusion-Bilinear Neural Network (ADB-NN) framework for brain network analysis, which is trained in an end-to-end manner and generates a joint representation of FC and SC for diagnosis.
Deep Representation Learning For Multimodal Brain Networks
TLDR
This work proposes a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks - DMBN) to fuse multimodal brain networks and decipher the cross-modality relationship through a graph encoding and decoding process.
Semi-Supervised Classification with Graph Convolutional Networks
TLDR
A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Deep Graph Infomax
TLDR
Deep Graph Infomax (DGI) is presented, a general approach for learning node representations within graph-structured data in an unsupervised manner that is readily applicable to both transductive and inductive learning setups.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture
TLDR
A connectivity-based parcellation framework is designed that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture and provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections.
DeepWalk: online learning of social representations
TLDR
DeepWalk is an online learning algorithm which builds useful incremental results, and is trivially parallelizable, which make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
A graph neural network framework for causal inference in brain networks
TLDR
The proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain and appears to be promising for the characterization of the information flow in brain networks.
Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia †
TLDR
The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
TLDR
Inspired by recent advances in deep metric learning (DML), this work carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data and closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders.
...
1
2
3
4
5
...