Area of research
Radiology, Nuclear Medicine and Imaging · Computer Vision and Pattern Recognition
Research interest
Research interests include Medical Image Segmentation Techniques, Advanced MRI Techniques and Applications, Cardiac Imaging and Diagnostics, and Advanced Neural Network Applications.
The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI
Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation
BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge
MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images
Multi-modality cardiac image computing: A survey
Multi-Source Domain Adaptation for Medical Image Segmentation
Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review
AWSnet: An auto-weighted supervision attention network for myocardial scar and edema segmentation in multi-sequence cardiac magnetic resonance images
Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
AttU-NET: Attention U-Net for Brain Tumor Segmentation
MyoPS-Net: Myocardial pathology segmentation with flexible combination of multi-sequence CMR images
Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation
Bayesian Image Super-Resolution With Deep Modeling of Image Statistics
A New Framework of Swarm Learning Consolidating Knowledge From Multi-Center Non-IID Data for Medical Image Segmentation
$\mathcal {X}$-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
Unsupervised Domain Adaptation With Variational Approximation for Cardiac Segmentation
Disentangle domain features for cross-modality cardiac image segmentation
Learning-based algorithms for vessel tracking: A review
AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-center LGE MRIs
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
CF Distance: A New Domain Discrepancy Metric and Application to Explicit Domain Adaptation for Cross-Modality Cardiac Image Segmentation
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network