Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

A Shoeibi, M Khodatars, M Jafari, N Ghassemi… - Information …, 2023 - Elsevier
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Deep learning for Alzheimer's disease diagnosis: A survey

M Khojaste-Sarakhsi, SS Haghighi… - Artificial intelligence in …, 2022 - Elsevier
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 …

A novel approach to large-scale dynamically weighted directed network representation

X Luo, H Wu, Z Wang, J Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
A dynamically weighted directed network (DWDN) is frequently encountered in various big
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 …

[HTML][HTML] A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

K Zhao, B Duka, H **e, DJ Oathes, V Calhoun, Y Zhang - NeuroImage, 2022 - Elsevier
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is
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

X Song, F Zhou, AF Frangi, J Cao… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
For significant memory concern (SMC) and mild cognitive impairment (MCI), their
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

Y Zong, Q Zuo, MKP Ng, B Lei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Brain network analysis plays an increasingly important role in studying brain function and
the exploring of disease mechanisms. However, existing brain network construction tools …

Adversarial learning based node-edge graph attention networks for autism spectrum disorder identification

Y Chen, J Yan, M Jiang, T Zhang… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Graph neural networks (GNNs) have received increasing interest in the medical imaging
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

K Qin, D Lei, WHL Pinaya, N Pan, W Li, Z Zhu… - …, 2022 - thelancet.com
Background Establishing objective and quantitative neuroimaging biomarkers at individual
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …

Gradient matching federated domain adaptation for brain image classification

LL Zeng, Z Fan, J Su, M Gan, L Peng… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
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 …