[HTML][HTML] Transfer learning approaches for neuroimaging analysis: a scoping review
Deep learning algorithms have been moderately successful in diagnoses of diseases by
analyzing medical images especially through neuroimaging that is rich in annotated data …
analyzing medical images especially through neuroimaging that is rich in annotated data …
[HTML][HTML] Application of deep learning for prediction of alzheimer's disease in PET/MR imaging
Y Zhao, Q Guo, Y Zhang, J Zheng, Y Yang, X Du… - Bioengineering, 2023 - mdpi.com
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …
Learned spatiotemporal correlation priors for CEST image denoising using incorporated global‐spectral convolution neural network
Purpose To develop a deep learning‐based method, dubbed Denoising CEST Network
(DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising …
(DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising …
An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis
Current imaging methods for diagnosing breast cancer (BC) are associated with limited
sensitivity and specificity and modest positive predictive power. The recent progress in …
sensitivity and specificity and modest positive predictive power. The recent progress in …
Transformer‐based deep learning denoising of single and multi‐delay 3D arterial spin labeling
Abstract Purpose To present a Swin Transformer‐based deep learning (DL) model (SwinIR)
for denoising single‐delay and multi‐delay 3D arterial spin labeling (ASL) and compare its …
for denoising single‐delay and multi‐delay 3D arterial spin labeling (ASL) and compare its …
[HTML][HTML] Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image
quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can …
quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can …
3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease
Alzheimer's is a progressive neurodegenerative disorder that leads to cognitive impairment
and ultimately death. To select the most effective treatment options, it is crucial to diagnose …
and ultimately death. To select the most effective treatment options, it is crucial to diagnose …
Motion correction in MRI using deep learning and a novel hybrid loss function
L Zhang, X Wang, M Rawson, R Balan… - arXiv preprint arXiv …, 2022 - arxiv.org
Purpose To develop and evaluate a deep learning-based method (MC-Net) to suppress
motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived …
motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived …
Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network
Purpose To reduce the total scan time of multiple postlabeling delay (multi‐PLD) pseudo‐
continuous arterial spin labeling (pCASL) by developing a hierarchically structured 3D …
continuous arterial spin labeling (pCASL) by developing a hierarchically structured 3D …
[HTML][HTML] Scoring method of English composition integrating deep learning in higher vocational colleges
S Feng, L Yu, F Liu - Scientific Reports, 2024 - nature.com
Along with the progress of natural language processing technology and deep learning, the
subjectivity, slow feedback, and long grading time of traditional English essay grading have …
subjectivity, slow feedback, and long grading time of traditional English essay grading have …