Area of research
Radiology, Nuclear Medicine and Imaging · Otorhinolaryngology
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, MRI in cancer diagnosis, Head and Neck Cancer Studies, and Medical Imaging Techniques and Applications.
Combining Multifrequency Magnetic Resonance Elastography With Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease
Deep learning-based computer-aided diagnostic system for lumbar degenerative diseases classification using MRI
A comprehensive dataset of magnetic resonance enterography images with intestinal segment annotations.
A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application
Deep learning-based detection of primary bone tumors around the knee joint on radiographs: a multicenter study
Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes.
A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application.
An interpretable two-branch bi-coordinate network based on multi-grained domain knowledge for classification of thyroid nodules in ultrasound images.
Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma
Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning
Serine-arginine protein kinase 1 (SRPK1) promotes EGFR-TKI resistance by enhancing GSK3β Ser9 autophosphorylation independent of its kinase activity in non-small-cell lung cancer
Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.
Improving Tumor Classification by Reusing Self-predicted Segmentation of Medical Images as Guiding Knowledge.
Intestinal fibrosis classification in patients with Crohn’s disease using CT enterography–based deep learning: comparisons with radiomics and radiologists
CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: a multicentre cohort study
Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn’s Disease
Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging
Preoperative Prediction of Cytokeratin 19 Expression for Hepatocellular Carcinoma with Deep Learning Radiomics Based on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging
Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms.
Voxel-based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis.
Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI
Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network
Multivoxel pattern analysis of structural MRI in children and adolescents with conduct disorder.
Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network.
Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study
Effect of 40 mg Versus 10 mg of Atorvastatin on Oxidized Low‐Density Lipoprotein, High‐Sensitivity C‐Reactive Protein, Circulating Endothelial‐Derived Microparticles, and Endothelial Progenitor Cells in Patients With Ischemic Cardiomyopathy