16 citations
- Catholic University of KoreaKR3 papers
- Asan Medical CenterKR1 paper
- BC Children's HospitalCA1 paper
- BC Research (Canada)CA1 paper
- IT University of CopenhagenDK1 paper
- Korea Advanced Institute of Science and TechnologyKR1 paper
- Korea Automotive Technology InstituteKR1 paper
- Korea UniversityKR1 paper
- Microsoft (United States)US1 paper
- National Institute of Diabetes and Digestive and Kidney DiseasesUS1 paper
- National Institutes of HealthUS1 paper
- National Institutes of Health Clinical CenterUS1 paper
4 papers
Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification
Benjamin Hou, Sung-Won Lee, Jung-Min Lee +4
Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian…
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
Shadab Ahamed, Yixi Xu, Sara Kurkowska +12
This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been a…
Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography
Yucheng Lu, Zhixin Xu, Moon Hyung Choi +2
Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential healt…
CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
Boah Kim, Dong Hwan Kim, Seong Ho Park +3
Image registration is a fundamental task in medical image analysis. Recently, deep learning based image registration methods have been extensively investigated due to their excelle…