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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 …
Deep learning for Alzheimer's disease diagnosis: A survey
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
A novel approach to large-scale dynamically weighted directed network representation
A dynamically weighted directed network (DWDN) is frequently encountered in various big
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
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 …
[HTML][HTML] A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …
Multicenter and multichannel pooling GCN for early AD diagnosis based on dual-modality fused brain network
For significant memory concern (SMC) and mild cognitive impairment (MCI), their
classification performance is limited by confounding features, diverse imaging protocols, and …
classification performance is limited by confounding features, diverse imaging protocols, and …
A new brain network construction paradigm for brain disorder via diffusion-based graph contrastive learning
Brain network analysis plays an increasingly important role in studying brain function and
the exploring of disease mechanisms. However, existing brain network construction tools …
the exploring of disease mechanisms. However, existing brain network construction tools …
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 …
Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites
Background Establishing objective and quantitative neuroimaging biomarkers at individual
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …
Gradient matching federated domain adaptation for brain image classification
Federated learning has shown its unique advantages in many different tasks, including brain
image analysis. It provides a new way to train deep learning models while protecting the …
image analysis. It provides a new way to train deep learning models while protecting the …