86 citations · 111 across the 6 of their papers we have counts for
10 papers
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
Ke Yan, Jinzheng Cai, Youjing Zheng +7
Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often eit…
ENet: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
Youbao Tang, Yuxing Tang, Yingying Zhu +2
Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment.…
Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model
Yingying Zhu, Youbao Tang, Yuxing Tang +4
Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image a…
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
Yifan Peng, Yu-Xing Tang, Sungwon Lee +3
The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collec…
Bone Suppression on Chest Radiographs With Adversarial Learning
Jia Liang, Yuxing Tang, Youbao Tang +2
Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characte…
TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays
Yuxing Tang, Youbao Tang, Veit Sandfort +2
In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains. Specifically, we study how to adapt the disease recognition m…