Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Youngtaek Hong

Showing results (11-20 of 28) with videos related to

Pageof 3
Sort By:
Computers in Biology and Medicine|April 28, 2023
Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoisingJina Lee, Jaeik Jeon, Youngtaek Hong, et al.
Journal of Cardiovascular Computed Tomography|April 8, 2025
Predicting categories of coronary artery calcium scores from chest X-ray images using deep learningYoungtaek Hong, Hyunseok Jeong, Younggul Jang, et al.
Korean Journal of Radiology|March 12, 2023
Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic SegmentationSeul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
European Radiology|November 14, 2018
Clinical feasibility of catheter-directed selective intracoronary computed tomography angiography using an extremely low dose of iodine in patients with coronary artery diseaseYoungtaek Hong, Hyung-Bok Park, Byoung Kwon Lee, et al.
Bioengineering (Basel, Switzerland)|January 8, 2025
Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) ImagesSeul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
Proceedings of Spie--The International Society for Optical Engineering|November 26, 2019
Deep learning-based stenosis quantification from coronary CT AngiographyYoungtaek Hong, Frederic Commandeur, Sebastien Cadet, et al.
The International Journal of Cardiovascular Imaging|April 23, 2024
Artificial intelligence-enhanced automation for M-mode echocardiographic analysis: ensuring fully automated, reliable, and reproducible measurementsDawun Jeong, Sunghee Jung, Yeonyee E Yoon, et al.
Yonsei Medical Journal|February 26, 2021
Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in SwineSeongmin Ha, Sunghee Jung, Hyung Bok Park, et al.
Yonsei Medical Journal|January 31, 2020
Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing SystemHyung Bok Park, Yeonggul Jang, Reza Arsanjani, et al.
Ebiomedicine|January 22, 2025
Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyJiesuck Park, Jiyeon Kim, Jaeik Jeon, et al.
Pageof 3

Showing results (11-20 of 28) with videos related to

Sort By:
Pageof 3
Computers in Biology and Medicine|April 28, 2023
Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoisingJina Lee, Jaeik Jeon, Youngtaek Hong, et al.
Journal of Cardiovascular Computed Tomography|April 8, 2025
Predicting categories of coronary artery calcium scores from chest X-ray images using deep learningYoungtaek Hong, Hyunseok Jeong, Younggul Jang, et al.
Korean Journal of Radiology|March 12, 2023
Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic SegmentationSeul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
European Radiology|November 14, 2018
Clinical feasibility of catheter-directed selective intracoronary computed tomography angiography using an extremely low dose of iodine in patients with coronary artery diseaseYoungtaek Hong, Hyung-Bok Park, Byoung Kwon Lee, et al.
Bioengineering (Basel, Switzerland)|January 8, 2025
Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) ImagesSeul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
Proceedings of Spie--The International Society for Optical Engineering|November 26, 2019
Deep learning-based stenosis quantification from coronary CT AngiographyYoungtaek Hong, Frederic Commandeur, Sebastien Cadet, et al.
The International Journal of Cardiovascular Imaging|April 23, 2024
Artificial intelligence-enhanced automation for M-mode echocardiographic analysis: ensuring fully automated, reliable, and reproducible measurementsDawun Jeong, Sunghee Jung, Yeonyee E Yoon, et al.
Yonsei Medical Journal|February 26, 2021
Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in SwineSeongmin Ha, Sunghee Jung, Hyung Bok Park, et al.
Yonsei Medical Journal|January 31, 2020
Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing SystemHyung Bok Park, Yeonggul Jang, Reza Arsanjani, et al.
Ebiomedicine|January 22, 2025
Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyJiesuck Park, Jiyeon Kim, Jaeik Jeon, et al.
Pageof 3