Using graph convolutional network to characterize individuals with ...

Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.

These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD.

Using graph convolutional network modeling to characterize ...

Advanced graph convolution network (GCN), as a deep learning technique which allows for direct convolution over graphs, may be one of the optimal model to ...

Using graph convolutional network to characterize individuals with ...

GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient ...

Using graph convolutional network to characterize individuals with ...

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites. EBioMedicine. Pub ...

RA-GCN: Graph convolutional network for disease prediction ...

Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the ...

Brain Connectivity Based Graph Convolutional Networks and Its ...

The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal ...

Graph Convolutional Networks Reveal Network-Level Functional ...

Therefore, the use of representative GCN classifiers on large-scale and multisite data can not only validate the feasibility of GNN methods for ...

Explaining graph convolutional network predictions for clinicians ...

The GCN model takes the feature vector and the normalized adjacency matrix and runs through a series of convolutional layers with updated network parameters, ...

(PDF) Graph Convolutional Networks and Functional Connectivity ...

are exceptionally intelligent and ready to live independently. ... deep learning methods: graph convolutional networks (GCNs). ... connectivity with a large multi- ...

Graph convolutional network with attention mechanism improve ...

Research paper. Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers ...

Graph Convolutional Networks —Deep Learning on Graphs

In this post we will see how the problem can be solved using Graph Convolutional Networks (GCN), which generalize classical Convolutional Neural Networks (CNN) ...

Graph convolutional network for fMRI analysis based on connectivity ...

In this paper, we describe a connectome-defined neighborhood for graph convolution to extract connectomic features from rs-fMRI data for ...

A novel spatiotemporal graph convolutional network framework for ...

Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD).

Deep Learning with Graph Convolutional Networks: An Overview ...

The original intention of modeling a GCN is to use the graph structure to describe the information aggregation of adjacent nodes, and the ...

Graph Convolutional Network with Morphometric Similarity ... - MICCAI

Graph convolutional networks (GCNs) provide great potential to improve schizophrenia classification using brain graphs derived from neuroimaging data. However, ...

The overall pipeline of GCN classifier distinguishing between...

The overall pipeline of GCN classifier distinguishing between individuals with MDD and HC. (a) Constructing graph structure for each participant using ...

Graph convolutional networks: a comprehensive review

Generally speaking, graph convolutional network models are a type of neural network architectures that can leverage the graph structure and ...

Introduction of Graph Convolutional Network (GCN) & Quick ...

For GCN, the goal is to learn a function of feature from a graph G=(V, E) and take as input: ... where W(l) is a weight matrix for the l-th neural ...

Graph Neural Network and Some of GNN Applications - neptune.ai

Explore Graph Neural Networks, from graph basics to deep learning concepts, Graph Convolutional Networks, and GNN applications.