313 citations
- Guangdong Provincial People's HospitalCN3 papers
- Chinese Academy of SciencesCN2 papers
- Guizhou Provincial People's HospitalCN2 papers
- South China University of TechnologyCN2 papers
- Beihang UniversityCN1 paper
- Beijing Academy of Artificial IntelligenceCN1 paper
- Carnegie Mellon UniversityUS1 paper
- Dalian University of TechnologyCN1 paper
- École de Technologie SupérieureCA1 paper
- Fudan UniversityCN1 paper
- Guangdong General HospitalCN1 paper
- Guangzhou First People's HospitalCN1 paper
6 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…