2 citations · 9 across the 16 of their papers we have counts for
23 papers
Iterative Data Refinement for Self-Supervised MR Image Reconstruction
Xue Liu, Juan Zou, Xiawu Zheng +3
Magnetic Resonance Imaging (MRI) has become an important technique in the clinic for the visualization, detection, and diagnosis of various diseases. However, one bottleneck limita…
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation
Yousuf Babiker M. Osman, Cheng Li, Weijian Huang +4
Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human…
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation
Cheng Li, Yousuf Babiker M. Osman, Weijian Huang +4
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic. Consequently, automatic volume-of-interest segmentation based on multi-parametric MR imagin…
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
Haoran Li, Cheng Li, Weijian Huang +3
Brain tumor segmentation based on multi-modal magnetic resonance imaging (MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and postoperative evaluations. D…
Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
Yeqi Wang, Weijian Huang, Cheng Li +3
Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-en…
Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging
Hui Tao, Haifeng Wang, Shanshan Wang +3
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise a…