38 citations
- Shenzhen Technology UniversityCN6 papers
- Beijing University of Posts and TelecommunicationsCN5 papers
- Chang Gung Memorial HospitalTW5 papers
- National Institutes of Health Clinical CenterUS4 papers
- Johns Hopkins UniversityUS3 papers
- University of Science and Technology of ChinaCN3 papers
- Association for Computing MachineryUS2 papers
- China Medical UniversityTW2 papers
- First Affiliated Hospital Zhejiang UniversityCN2 papers
- National Tsing Hua UniversityTW2 papers
- Changhai HospitalCN1 paper
- Computer Network Information CenterCN1 paper
5 papers · 1 filter
Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
Jinzheng Cai, Ke Yan, Chi-Tung Cheng +4
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/…
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo +10
Determining the spread of GTV is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy fo…
Learning to Segment Anatomical Structures Accurately from One Exemplar
Yuhang Lu, Weijian Li, Kang Zheng +8
Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotatio…
High Frequency Residual Learning for Multi-Scale Image Classification
Bowen Cheng, Rong Xiao, Jianfeng Wang +2
We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. T…
Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier
Yuxing Tang, Youbao Tang, Mei Han +2
Being one of the most common diagnostic imaging tests, chest radiography requires timely reporting of potential findings in the images. In this paper, we propose an end-to-end arch…