313 citations · 719 across the 30 of their papers we have counts for
23 papers · 1 filter
Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation
Yousuf Babiker M. Osman, Cheng Li, Weijian Huang +1
Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotati…
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…
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…
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…
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…
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…
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…
Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study
Longfei Li, Rui Yang, Xin Chen +5
Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classiêes patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance. Howev…
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…
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
Kehan Qi, Haoran Li, Chuyu Rong +4
Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-qua…
Annotation-efficient deep learning for automatic medical image segmentation
Shanshan Wang, Cheng Li, Rongpin Wang +12
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training…
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint
Weijian Huang, Hao Yang, Xinfeng Liu +5
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless,…
Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging
Yanxia Chen, Taohui Xiao, Cheng Li +2
Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated w…
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
Cheng Li, Hui Sun, Zaiyi Liu +3
Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tend…
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
Hao Yang, Weijian Huang, Kehan Qi +5
Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with th…
X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies
Kehan Qi, Hao Yang, Cheng Li +4
The morbidity of brain stroke increased rapidly in the past few years. To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critica…