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 …
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 …
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 …
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 …
Classifying ASD based on time-series fMRI using spatial–temporal transformer
As the prevalence of autism spectrum disorder (ASD) increases globally, more and more
patients need to receive timely diagnosis and treatment to alleviate their suffering. However …
patients need to receive timely diagnosis and treatment to alleviate their suffering. However …
MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder
J Pan, H Lin, Y Dong, Y Wang, Y Ji - Computers in biology and medicine, 2022 - Elsevier
Purpose Existing diagnoses of mental disorders rely on symptoms, patient descriptions, and
scales, which are not objective enough. We attempt to explore an objective diagnostic …
scales, which are not objective enough. We attempt to explore an objective diagnostic …
Contrastive brain network learning via hierarchical signed graph pooling model
Recently, brain networks have been widely adopted to study brain dynamics, brain
development, and brain diseases. Graph representation learning techniques on brain …
development, and brain diseases. Graph representation learning techniques on brain …
CNNG: a convolutional neural networks with gated recurrent units for autism spectrum disorder classification
As a neurodevelopmental disorder, autism spectrum disorder (ASD) severely affects the
living conditions of patients and their families. Early diagnosis of ASD can enable the …
living conditions of patients and their families. Early diagnosis of ASD can enable the …
Instance importance-aware graph convolutional network for 3D medical diagnosis
Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By
exploiting the abundant pathological information of 3D data, human experts and algorithms …
exploiting the abundant pathological information of 3D data, human experts and algorithms …