4 citations · 6 across the 3 of their papers we have counts for
6 papers
Lymph Node Detection in T2 MRI with Transformers
Tejas Sudharshan Mathai, Sungwon Lee, Daniel C. Elton +4
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases. The…
Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation
Seung Yeon Shin, Sungwon Lee, Ronald M. Summers
We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentan…
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…
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…
Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection
Yingying Zhu, Daniel C. Elton, Sungwon Lee +2
Calcified plaque in the aorta and pelvic arteries is associated with coronary artery calcification and is a strong predictor of heart attack. Current calcified plaque detection mod…