3.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National Institutes of HealthUS40 papers
- National Cancer InstituteUS8 papers
- Ping An (China)8 papers
- National Center for Biotechnology InformationUS7 papers
- Nvidia (United States)US7 papers
- University of PennsylvaniaUS5 papers
- Vanderbilt UniversityUS5 papers
- German Cancer Research CenterDE4 papers
- Heidelberg UniversityDE4 papers
- Imperial College LondonGB4 papers
- King's College LondonGB4 papers
- Radboud University Medical CenterNL4 papers
9 papers · 2 filters
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
Dong Yang, Ziyue Xu, Wenqi Li +17
The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able t…
One Click Lesion RECIST Measurement and Segmentation on CT Scans
Youbao Tang, Ke Yan, Jing Xiao +1
In clinical trials, one of the radiologists' routine work is to measure tumor sizes on medical images using the RECIST criteria (Response Evaluation Criteria In Solid Tumors). Howe…
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.…
Deep Small Bowel Segmentation with Cylindrical Topological Constraints
Seung Yeon Shin, Sungwon Lee, Daniel C. Elton +2
We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could…
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…
Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
R. Robinson, Q. Dou, D. C. Castro +5
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed wi…
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…
Accurately identifying vertebral levels in large datasets
Daniel C. Elton, Veit Sandfort, Perry J. Pickhardt +1
The vertebral levels of the spine provide a useful coordinate system when making measurements of plaque, muscle, fat, and bone mineral density. Correctly classifying vertebral leve…