540 citations
- Chinese Academy of SciencesCN14 papers
- Institute of Computing TechnologyCN12 papers
- University of Chinese Academy of SciencesCN9 papers
- Tsinghua UniversityCN6 papers
- Shanghai Jiao Tong UniversityCN5 papers
- City University of Hong KongHK3 papers
- Fudan UniversityCN3 papers
- Hefei University of TechnologyCN3 papers
- South China University of TechnologyCN3 papers
- University of California, Los AngelesUS3 papers
- Zhejiang UniversityCN3 papers
- Beijing Jiaotong UniversityCN2 papers
7 papers · 1 filter
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
Yao Zhang, Jiawei Yang, Feng Hou +6
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However,…
Double-Uncertainty Weighted Method for Semi-supervised Learning
Yixin Wang, Yao Zhang, Jiang Tian +4
Though deep learning has achieved advanced performance recently, it remains a challenging task in the field of medical imaging, as obtaining reliable labeled training data is time-…
Modality-Pairing Learning for Brain Tumor Segmentation
Yixin Wang, Yao Zhang, Feng Hou +5
Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment…
Acoustic Word Embedding System for Code-Switching Query-by-example Spoken Term Detection
Murong Ma, Haiwei Wu, Xuyang Wang +3
In this paper, we propose a deep convolutional neural network-based acoustic word embedding system on code-switching query by example spoken term detection. Different from previous…
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System
Xinyue Zheng, Peng Wang, Qigang Wang +1
Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia. However, retrieving knowledge fr…
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Jun Ma, Yixin Wang, Xingle An +10
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based…