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 …
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 (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity
Brain connectivity alterations associated with mental disorders have been widely reported in
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
A survey on deep learning for neuroimaging-based brain disorder analysis
Deep learning has recently been used for the analysis of neuroimages, such as structural
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
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 …
Graph signal processing, graph neural network and graph learning on biological data: a systematic review
Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …
span from population graphs (with patients as network nodes) to molecular graphs that …
Diagnosis of autism spectrum disorder based on functional brain networks with deep learning
Autism spectrum disorder (ASD) is a neurological and developmental disorder. Traditional
diagnosis of ASD is typically performed through the observation of behaviors and interview …
diagnosis of ASD is typically performed through the observation of behaviors and interview …
[HTML][HTML] A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity
This paper presents a comprehensive and practical review of autism spectrum disorder
(ASD) classification using several traditional machine learning and deep learning methods …
(ASD) classification using several traditional machine learning and deep learning methods …
Adversarial learning based node-edge graph attention networks for autism spectrum disorder identification
Graph neural networks (GNNs) have received increasing interest in the medical imaging
field given their powerful graph embedding ability to characterize the non-Euclidean …
field given their powerful graph embedding ability to characterize the non-Euclidean …
Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI
Brain functional connectivity (FC) derived from resting-state functional magnetic resonance
imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as …
imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as …