Area of research
Computer Vision and Pattern Recognition · Electrical and Electronic Engineering
Research interest
Research interests include Perovskite Materials and Applications, Radiomics and Machine Learning in Medical Imaging, Advanced Neural Network Applications, and Medical Image Segmentation Techniques.
Toward Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.
EndoClean: A Hybrid Deep Learning Framework for Automated Full-Video Boston Bowel Preparation Scale Assessment.
Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.
Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses
Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses.
The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.
CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI.
A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.
Robust Polyp Detection and Diagnosis Through Compositional Prompt-Guided Diffusion Models.
Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.
On-the-Fly Improving Segment Anything for Medical Image Segmentation Using Auxiliary Online Learning
Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.
Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI.
Expert-AI Collaborative Training for Novice Endoscopists: A Path to Enhanced Efficiency.
Artificial intelligence assisted identification of newborn auricular deformities via smartphone application.
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI
Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.
STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution
Tumor contour irregularity on preoperative CT predicts prognosis in renal cell carcinoma: a multi-institutional study.
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.
STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.
Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation
A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.
TestFit: A plug-and-play one-pass test time method for medical image segmentation.
A Protocol for Body MRI/CT and Extraction of Imaging-Derived Phenotypes (IDPs) from the China Phenobank Project.
A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart Dynamics
Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction
Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence
Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence.