Multi-task deep learning for medical image computing and analysis: A review
The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …
conventional deep learning models are constructed for a single specific task, multi-task deep …
Deep learning in large and multi-site structural brain MR imaging datasets
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation, and testing of advanced deep learning (DL)-based automated tools …
training, validation, and testing of advanced deep learning (DL)-based automated tools …
Transformed domain convolutional neural network for Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has become a prevalent and potent imaging
modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia …
modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia …
Spatial–temporal co-attention learning for diagnosis of mental disorders from resting-state fMRI data
Neuroimaging techniques have been widely adopted to detect the neurological brain
structures and functions of the nervous system. As an effective noninvasive neuroimaging …
structures and functions of the nervous system. As an effective noninvasive neuroimaging …
Alzheimer's disease diagnosis with brain structural mri using multiview-slice attention and 3D convolution neural network
Numerous artificial intelligence (AI) based approaches have been proposed for automatic
Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging …
Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging …
PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning
The continuous measurement of blood pressure (BP) plays an important role in preventing
cardiovascular diseases. However, common cuff-based devices with cumbersome …
cardiovascular diseases. However, common cuff-based devices with cumbersome …
Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection
B Lei, Y Liang, J Xie, Y Wu, E Liang, Y Liu, P Yang… - Pattern Recognition, 2024 - Elsevier
Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection
remains a challenge. AD detection using multi-center datasets can expand the sample size …
remains a challenge. AD detection using multi-center datasets can expand the sample size …
Community graph convolution neural network for alzheimer's disease classification and pathogenetic factors identification
XA Bi, K Chen, S Jiang, S Luo, W Zhou… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
As a complex neural network system, the brain regions and genes collaborate to effectively
store and transmit information. We abstract the collaboration correlations as the brain region …
store and transmit information. We abstract the collaboration correlations as the brain region …
Attention-like multimodality fusion with data augmentation for diagnosis of mental disorders using MRI
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and
therapy to reduce patients' suffering. Facing such an urgent public health problem …
therapy to reduce patients' suffering. Facing such an urgent public health problem …
A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study
Background Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses
a worldwide public health challenge. A neuroimaging biomarker would significantly improve …
a worldwide public health challenge. A neuroimaging biomarker would significantly improve …