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09/15/2023
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Aims. The purpose of this study is to create a multi-stage machine learn...
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08/20/2023
Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
Coronary artery disease (CAD) is one of the primary causes leading to de...
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06/29/2023
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
The privacy protection mechanism of federated learning (FL) offers an ef...
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06/02/2023
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Background: Cardiac resynchronization therapy (CRT) has emerged as an ef...
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05/21/2023
Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network
Coronary artery disease (CAD) is one of the primary causes leading death...
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05/04/2023
A new method using deep learning to predict the response to cardiac resynchronization therapy
Background. Clinical parameters measured from gated single-photon emissi...
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01/29/2023
A novel method using machine learning to integrate features from lung and epicardial adipose tissue for detecting the severity of COVID-19 infection
Objectives: To investigate the value of radiomics features of epicardial...
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01/11/2023
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms
Semantic labeling of coronary arterial segments in invasive coronary ang...
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10/03/2022
Hip Fracture Prediction using the First Principal Component Derived from FEA-Computed Fracture Loads
Hip fracture risk assessment is an important but challenging task. Quant...
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10/03/2022
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
Background and aim: Hip fracture can be devastating. The proximal femora...
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06/24/2022
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms
Accurate extraction of coronary arteries from invasive coronary angiogra...
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06/07/2022
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Background: The assessment of left ventricular (LV) function by myocardi...
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02/21/2022
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation
Network pruning and knowledge distillation are two widely-known model co...
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10/11/2021
Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images
Background. Functional assessment of right ventricles (RV) using gated m...
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10/06/2021
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos
Automatic identification of proper image frames at the end-diastolic (ED...
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06/01/2021
A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI
Background. Studies have shown that the conventional left ventricular me...
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02/21/2021
A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images
Intravascular ultrasound (IVUS) imaging allows direct visualization of t...
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02/03/2021
A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images
Automatic CT segmentation of proximal femur is crucial for the diagnosis...
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01/25/2021
A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms
In stable coronary artery disease (CAD), reduction in mortality and/or m...
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06/09/2020