313 citations · 518 across the 35 of their papers we have counts for
36 papers
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search
Ruoyou Wu, Cheng Li, Juan Zou +1
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstructio…
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction
Ruoyou Wu, Cheng Li, Juan Zou +1
Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions. Federated learning,…
Self-Supervised Federated Learning for Fast MR Imaging
Juan Zou, Cheng Li, Ruoyou Wu +3
Federated learning (FL) based magnetic resonance (MR) image reconstruction can facilitate learning valuable priors from multi-site institutions without violating patient's privacy…
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data
Ruoyou Wu, Cheng Li, Juan Zou +3
Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robu…
Few-shot Class-incremental Learning for Cross-domain Disease Classification
Hao Yang, Weijian Huang, Jiarun Liu +2
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application. Although existin…
MGA: Medical generalist agent through text-guided knowledge transformation
Weijian Huang, Hao Yang, Cheng Li +3
Multi-modal representation methods have achieved advanced performance in medical applications by extracting more robust features from multi-domain data. However, existing methods u…
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…
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Juan Zou, Cheng Li, Sen Jia +4
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampl…
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
Hong Peng, Chen Jiang, Jing Cheng +4
Magnetic resonance imaging serves as an essential tool for clinical diagnosis. However, it suffers from a long acquisition time. The utilization of deep learning, especially the de…
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Zongjiang Tu, Die Liu, Xiaoqing Wang +6
Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still has some limitations, suc…
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…
Rethinking the optimization process for self-supervised model-driven MRI reconstruction
Weijian Huang, Cheng Li, Wenxin Fan +4
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction. Recently, various supervised deep learning-based MRI reconstruction me…
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Zongjiang Tu, Chen Jiang, Yu Guan +4
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to…
PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging
Shanshan Wang, Ruoyou Wu, Cheng Li +5
With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in…
Universal Generative Modeling for Calibration-free Parallel Mr Imaging
Wanqing Zhu, Bing Guan, Shanshan Wang +2
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit…
Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading
Yeqi Wang, Longfei Li, Cheng Li +4
Radiomics and deep learning have shown high popularity in automatic glioma grading. Radiomics can extract hand-crafted features that quantitatively describe the expert knowledge of…
Variable Augmented Network for Invertible MR Coil Compression
Xianghao Liao, Shanshan Wang, Lanlan Tu +3
A large number of coils are able to provide enhanced signal-to-noise ratio and improve imaging performance in parallel imaging. Nevertheless, the increasing growth of coil number s…
A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images
Zijie Chen, Cheng Li, Junjun He +5
Radiation therapy (RT) is widely employed in the clinic for the treatment of head and neck (HaN) cancers. An essential step of RT planning is the accurate segmentation of various o…
Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation
Junjun He, Jin Ye, Cheng Li +5
Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images. Compared with the 2D counterparts, 3D convolutions can capture the spati…
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
Chen Hu, Cheng Li, Haifeng Wang +3
Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited…