A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Graph neural networks and their current applications in bioinformatics
XM Zhang, L Liang, L Liu, MJ Tang - Frontiers in genetics, 2021 - frontiersin.org
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …
perform particularly well in various tasks that process graph structure data. With the rapid …
Braingnn: Interpretable brain graph neural network for fmri analysis
Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …
cognitive stimuli has been an important area of neuroimaging research. We propose …
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 …
Graph neural networks in network neuroscience
A Bessadok, MA Mahjoub… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Noninvasive medical neuroimaging has yielded many discoveries about the brain
connectivity. Several substantial techniques map** morphological, structural and …
connectivity. Several substantial techniques map** morphological, structural and …
Braingb: a benchmark for brain network analysis with graph neural networks
Map** the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
Purpose Recently, brain connectivity networks have been used for the classification of
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
Brain network transformer
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their
connections for the understanding of brain functions and mental disorders. Recently …
connections for the understanding of brain functions and mental disorders. Recently …
Learning dynamic graph representation of brain connectome with spatio-temporal attention
Functional connectivity (FC) between regions of the brain can be assessed by the degree of
temporal correlation measured with functional neuroimaging modalities. Based on the fact …
temporal correlation measured with functional neuroimaging modalities. Based on the fact …
Spatio-temporal graph convolution for resting-state fMRI analysis
Abstract The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI)
records the temporal dynamics of intrinsic functional networks in the brain. However, existing …
records the temporal dynamics of intrinsic functional networks in the brain. However, existing …