Area of research
Pulmonary and Respiratory Medicine · Molecular Biology
Research interest
Research interests include Lung Cancer Treatments and Mutations, RNA modifications and cancer, Bone health and osteoporosis research, and Cardiac Imaging and Diagnostics.
An uncertainty-aware dynamic decision framework for progressive multi-omics integration in classification tasks.
FAT-Net: Frequency-Domain Attention-Guided Topology-Refinement Network for Coronary Artery Segmentation in Invasive Coronary Angiography.
Multi-Cohort Structural Magnetic Resonance Imaging-Based Alzheimer’s Disease Staging Using Convolution Neural Network
Computed Fluid Dynamics-Based Blood Pressure Prediction for Coronary Artery Disease Diagnosis Using Coronary Computed Tomography Angiography
Integrated multi-omics analysis reveals PTM networks as key regulators of colorectal cancer progression and immune evasion
Staged Multi-Omics Data Classification for Breast Cancer Diagnosis
A Hybrid Approach of Data Mining and Deep Learning for Network Intrusion Detection
Clinical features, polysomnography, and genetics association study of restless legs syndrome in clinic based Chinese patients: A multicenter observational study
AGFI-GAN: An Attention-Guided and Feature-Integrated Watermarking Model based on GAN Framework for Secure and Auditable Medical Imaging Application
Longitudinal patterns of abdominal visceral and subcutaneous adipose tissue, total body composition, and anthropometric measures in postmenopausal women: Results from the Women’s Health Initiative
Research on the development methodology for clinical practice guidelines for organic integration of traditional Chinese and Western medicine
ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images.
A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.
Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study.
Association of ß-glucuronidase activity with menopausal status, ethnicity, adiposity, and inflammation in women
Mortality Following Hip Fracture in Older Adults With and Without Coronary Heart Disease
Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT.
Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.
Spatial‐temporal V‐Net for automatic segmentation and quantification of right ventricle on gated myocardial perfusion SPECT images
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms.
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms.
EAGMN: Coronary artery semantic labeling using edge attention graph matching network.
Spatial-temporal V-Net for automatic segmentation and quantification of right ventricle on gated myocardial perfusion SPECT images.
Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.
3D fusion between SPECT myocardial perfusion imaging and invasive coronary angiography to guide the treatment for patients with stable CAD
Intestinal fibrosis classification in patients with Crohn’s disease using CT enterography–based deep learning: comparisons with radiomics and radiologists
Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs
Use of oral diabetes medications and the risk of incident dementia in US veterans aged ≥60 years with type 2 diabetes
A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.