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 …
Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
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 …
BrainTGL: A dynamic graph representation learning model for brain network analysis
Modeling the dynamics characteristics in functional brain networks (FBNs) is important for
understanding the functional mechanism of the human brain. However, the current works do …
understanding the functional mechanism of the human brain. However, the current works do …
Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data
P Dai, D Lu, Y Shi, Y Zhou, T **ong, X Zhou… - Journal of Affective …, 2023 - Elsevier
Background Major depressive disorder (MDD) has a high rate of recurrence. Identifying
patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the …
patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the …
[HTML][HTML] Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development
Modeling dynamic interactions among network components is crucial to uncovering the
evolution mechanisms of complex networks. Recently, spatio-temporal graph learning …
evolution mechanisms of complex networks. Recently, spatio-temporal graph learning …
[HTML][HTML] DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique
pivotal for understanding human neural mechanisms of intricate cognitive processes. Most …
pivotal for understanding human neural mechanisms of intricate cognitive processes. Most …
A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions
Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …
analysis and modeling of intricate relationships present in data structured as graphs. In …
Unsupervised contrastive graph learning for resting‐state functional MRI analysis and brain disorder detection
Resting‐state functional magnetic resonance imaging (rs‐fMRI) helps characterize regional
interactions that occur in the human brain at a resting state. Existing research often attempts …
interactions that occur in the human brain at a resting state. Existing research often attempts …
Classification of MDD using a Transformer classifier with large‐scale multisite resting‐state fMRI data
Major depressive disorder (MDD) is one of the most common psychiatric disorders
worldwide with high recurrence rate. Identifying MDD patients, particularly those with …
worldwide with high recurrence rate. Identifying MDD patients, particularly those with …