DeepThink IoT: the strength of deep learning in internet of things
Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …
revolutionized technology in the twenty-first century, enabling humans and machines to …
A generic framework for embedding human brain function with temporally correlated autoencoder
Learning an effective and compact representation of human brain function from high-
dimensional fMRI data is crucial for studying the brain's functional organization. Traditional …
dimensional fMRI data is crucial for studying the brain's functional organization. Traditional …
A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks
N Qiang, J Gao, Q Dong, J Li, S Zhang, H Liang… - Behavioural Brain …, 2023 - Elsevier
Background It has been recently shown that deep learning models exhibited remarkable
performance of representing functional Magnetic Resonance Imaging (fMRI) data for the …
performance of representing functional Magnetic Resonance Imaging (fMRI) data for the …
Functional brain network identification and fMRI augmentation using a VAE-GAN framework
N Qiang, J Gao, Q Dong, H Yue, H Liang, L Liu… - Computers in Biology …, 2023 - Elsevier
Recently, deep learning models have achieved superior performance for mapping functional
brain networks from functional magnetic resonance imaging (fMRI) data compared with …
brain networks from functional magnetic resonance imaging (fMRI) data compared with …
A novel ADHD classification method based on resting state temporal templates (RSTT) using spatiotemporal attention auto-encoder
It has been of great interest in the neuroimaging community to model spatiotemporal brain
function and related disorders based on resting state functional magnetic resonance …
function and related disorders based on resting state functional magnetic resonance …
Multi-head attention-based masked sequence model for mapping functional brain networks
M He, X Hou, E Ge, Z Wang, Z Kang, N Qiang… - Frontiers in …, 2023 - frontiersin.org
The investigation of functional brain networks (FBNs) using task-based functional magnetic
resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging …
resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging …
Learning brain representation using recurrent Wasserstein generative adversarial net
Background and objective To understand brain cognition and disorders, modeling the
mapping between mind and brain has been of great interest to the neuroscience community …
mapping between mind and brain has been of great interest to the neuroscience community …
Discovering dynamic functional brain networks via spatial and channel-wise attention
Using deep learning models to recognize functional brain networks (FBNs) in functional
magnetic resonance imaging (fMRI) has been attracting increasing interest recently …
magnetic resonance imaging (fMRI) has been attracting increasing interest recently …
Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder
Objective. Recently, deep learning models have been successfully applied in functional
magnetic resonance imaging (fMRI) modeling and associated applications. However, there …
magnetic resonance imaging (fMRI) modeling and associated applications. However, there …
[HTML][HTML] Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI
Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role
in understanding human brain function. There are many proposed methods for mapping the …
in understanding human brain function. There are many proposed methods for mapping the …