Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence
Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …
powerful modeling capabilities and have been successfully applied in natural language …
Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis
Purpose Recently, functional brain networks (FBN) have been used for the classification of
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
Classification of brain disorders in rs-fMRI via local-to-global graph neural networks
H Zhang, R Song, L Wang, L Zhang… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Recently, functional brain network has been used for the classification of brain disorders,
such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods …
such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods …
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review
SL Warren, AA Moustafa - Journal of Neuroimaging, 2023 - Wiley Online Library
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and
clinical observations. However, these diagnoses are not perfect, and additional diagnostic …
clinical observations. However, these diagnoses are not perfect, and additional diagnostic …
A survey on graph neural networks and graph transformers in computer vision: a task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …
Fedni: Federated graph learning with network inpainting for population-based disease prediction
Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis.
Specifically, in medical applications, GCNs can be used for disease prediction on a …
Specifically, in medical applications, GCNs can be used for disease prediction on a …
A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders
Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-
consuming cognitive tests and potential human bias in clinics. To address this challenge, we …
consuming cognitive tests and potential human bias in clinics. To address this challenge, we …
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques enable us to construct human brains as brain networks or
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
connectomes. Capturing brain networks' structural information and hierarchical patterns is …