[HTML][HTML] Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional …
Abstract Background Cine Displacement Encoding with Stimulated Echoes (DENSE)
facilitates the quantification of myocardial deformation, by encoding tissue displacements in …
facilitates the quantification of myocardial deformation, by encoding tissue displacements in …
Cardiac magnetic resonance radiomics for disease classification
X Zhang, C Cui, S Zhao, L Xie, Y Tian - European Radiology, 2023 - Springer
Objectives This study investigated the discriminability of quantitative radiomics features
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …
A stacked long short-term memory approach for predictive blood glucose monitoring in women with gestational diabetes mellitus
Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during
pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce …
pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce …
[HTML][HTML] Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior
Myocardial motion and deformation are rich descriptors that characterize cardiac function.
Image registration, as the most commonly used technique for myocardial motion tracking, is …
Image registration, as the most commonly used technique for myocardial motion tracking, is …
Classification of tetanus severity in intensive-care settings for low-income countries using wearable sensing
Infectious diseases remain a common problem in low-and middle-income countries,
including in Vietnam. Tetanus is a severe infectious disease characterized by muscle …
including in Vietnam. Tetanus is a severe infectious disease characterized by muscle …
Multiscale graph convolutional networks for cardiac motion analysis
We propose a multiscale spatio-temporal graph convolutional network (MST-GCN)
approach to learn the left ventricular (LV) motion patterns from cardiac MR image …
approach to learn the left ventricular (LV) motion patterns from cardiac MR image …
Modelling cardiac motion via spatio-temporal graph convolutional networks to boost the diagnosis of heart conditions
We present a novel spatio-temporal graph convolutional networks (ST-GCN) approach to
learn spatio-temporal patterns of left ventricular (LV) motion in cardiac MR cine images for …
learn spatio-temporal patterns of left ventricular (LV) motion in cardiac MR cine images for …
[HTML][HTML] A Deep Learning Approach of Blood Glucose Predictive Monitoring for Women with Gestational Diabetes
Gestational diabetes is a subtype of diabetes that develops during pregnancy. Managing
blood glucose (BG) within the healthy physiological range can reduce clinical complications …
blood glucose (BG) within the healthy physiological range can reduce clinical complications …
Deep Learning in Cardiac Magnetic Resonance Image Analysis and Cardiovascular Disease Diagnosis
X Chen - 2023 - etheses.whiterose.ac.uk
Cardiovascular diseases (CVDs) are the leading cause of death in the world, accounting for
17.9 million deaths each year, 31\% of all global deaths. According to the World Health …
17.9 million deaths each year, 31\% of all global deaths. According to the World Health …
Learning structure-function interactions of the heart using generative deep learning methods
J Ossenberg-Engels - 2021 - ora.ox.ac.uk
Cardiovascular diseases account for the highest number of annual deaths worldwide, a
burden exacerbated by current limitations in disease understanding. Accurate clinical …
burden exacerbated by current limitations in disease understanding. Accurate clinical …