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Investigative Radiology
|
November 28, 2021
Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features: A Phantom Study
Seul Bi Lee, Yeon Jin Cho, Youngtaek Hong, et al.
Computers in Biology and Medicine
|
April 28, 2023
Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising
Jina Lee, Jaeik Jeon, Youngtaek Hong, et al.
Korean Journal of Radiology
|
March 12, 2023
Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation
Seul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
European Heart Journal. Digital Health
|
March 9, 2026
Deep learning-based multi-view echocardiographic framework for comprehensive diagnosis of pericardial disease
Sihyeon Jeong, In Tae Moon, Jaeik Jeon, 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 measurements
Dawun Jeong, Sunghee Jung, Yeonyee E Yoon, et al.
Bioengineering (Basel, Switzerland)
|
January 8, 2025
Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images
Seul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography
|
June 15, 2026
Deep Learning-Based Multiclass Classification of Mitral Valve Etiologies Using Limited B-Mode and Color Doppler Echocardiography: Internal and External Validation
Dawun Jeong, Moon-Seung Soh, Jaeik Jeon, et al.
Circulation. Cardiovascular Imaging
|
March 27, 2025
Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation
Inki Moon, Jina Lee, Seung-Ah Lee, et al.
Korean Circulation Journal
|
October 22, 2024
An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI
Yeonggul Jang, Hyejung Choi, Yeonyee E Yoon, et al.
Cardiovascular Diagnosis and Therapy
|
July 8, 2024
Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction
Jiesuck Park, Jaeik Jeon, Yeonyee E Yoon, et al.
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Search research articles
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Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
Investigative Radiology
|
November 28, 2021
Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features: A Phantom Study
Seul Bi Lee, Yeon Jin Cho, Youngtaek Hong, et al.
Computers in Biology and Medicine
|
April 28, 2023
Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising
Jina Lee, Jaeik Jeon, Youngtaek Hong, et al.
Korean Journal of Radiology
|
March 12, 2023
Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation
Seul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
European Heart Journal. Digital Health
|
March 9, 2026
Deep learning-based multi-view echocardiographic framework for comprehensive diagnosis of pericardial disease
Sihyeon Jeong, In Tae Moon, Jaeik Jeon, 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 measurements
Dawun Jeong, Sunghee Jung, Yeonyee E Yoon, et al.
Bioengineering (Basel, Switzerland)
|
January 8, 2025
Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images
Seul Bi Lee, Youngtaek Hong, Yeon Jin Cho, et al.
Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography
|
June 15, 2026
Deep Learning-Based Multiclass Classification of Mitral Valve Etiologies Using Limited B-Mode and Color Doppler Echocardiography: Internal and External Validation
Dawun Jeong, Moon-Seung Soh, Jaeik Jeon, et al.
Circulation. Cardiovascular Imaging
|
March 27, 2025
Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation
Inki Moon, Jina Lee, Seung-Ah Lee, et al.
Korean Circulation Journal
|
October 22, 2024
An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI
Yeonggul Jang, Hyejung Choi, Yeonyee E Yoon, et al.
Cardiovascular Diagnosis and Therapy
|
July 8, 2024
Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction
Jiesuck Park, Jaeik Jeon, Yeonyee E Yoon, et al.
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