313 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN4 papers
- Guangdong Provincial People's HospitalCN4 papers
- Guangdong General HospitalCN3 papers
- Henan Provincial People's HospitalCN3 papers
- Shenzhen Institutes of Advanced TechnologyCN3 papers
- Fudan UniversityCN2 papers
- Guangzhou UniversityCN2 papers
- Guizhou Provincial People's HospitalCN2 papers
- Peng Cheng LaboratoryCN2 papers
- South China University of TechnologyCN2 papers
- Southern Medical UniversityCN2 papers
- University of Chinese Academy of SciencesCN2 papers
9 papers
3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation
Xiaoyang Qin, Hao Huang, Shuaichen Lin +8
Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent appositio…
Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI
Lei Zhou, Yuzhong Zhang, Jiadong Zhang +8
Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for…
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
Lingwei Meng, Di Dong, Xin Chen +5
Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole…
Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis
Yutian Chen, Xiaowei Xu, Dewen Zeng +6
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) s…
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…
TENet: Triple Excitation Network for Video Salient Object Detection
Sucheng Ren, Chu Han, Xin Yang +2
In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, s…
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…
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…
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
Jiawei Zhang, Yuzhen Jin, Jilan Xu +2
Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) an…