Search Results - "Jong Hyo Kim"

Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.

Alternate Title: 벤더 중립 딥러닝 모델을 이용한 추가 선량 절감 평가 : 팬텀 기반 연구.

Authors: Kim, Jisu Chang, Won Kim, Jong Hyo et al.

Source: Journal of the Korean Society of Radiology (2951-0805). May2026, Vol. 87 Issue 3, p531-545. 15p.

Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT.

Authors: Kim, Sihwan Park, Eun-Ah Ahn, Chulkyun et al.

Source: European Radiology. Nov2025, Vol. 35 Issue 11, p7084-7095. 12p.

Subjects: Coronary artery calcification, Computed tomography, Deep learning, Artificial intelligence, Evaluation methodology

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

Authors: Lee, Myungeun Kim, Jong Hyo Choi, Wookjin et al.

Source: Journal of Imaging Informatics in Medicine; Feb2025, Vol. 38 Issue 1, p74-83, 10p

Improved Consistency of Lung Nodule Categorization in CT Scans with Heterogeneous Slice Thickness by Deep Learning-Based 3D Super-Resolution.

Authors: Kim, Dongok Park, Jae Hyung Lee, Chang Hyun et al.

Source: Diagnostics (2075-4418). Jan2025, Vol. 15 Issue 1, p50. 14p.

Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI.

Authors: Sang Ho Lee Jong Hyo Kim Nariya Cho et al.

Source: Medical Physics. Aug2010, Vol. 37 Issue 8, p3940-3956. 17p. 1 Color Photograph, 2 Black and White Photographs, 1 Diagram, 5 Charts, 6 Graphs.

Subjects: Medical imaging systems, Tumor growth, Breast tumors, Differential diagnosis, Magnetic resonance imaging

Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.

Authors: Lee, Jemyoung Park, Heejun Yang, Zepa et al.

Source: Diagnostics (2075-4418). Nov2024, Vol. 14 Issue 22, p2477. 14p.

빛나는 성과의 기록: 우수논문· 심사자상· 최다 인용· 최다 조회.

Alternate Title: Records of Excellence: Award-Winning Papers, Reviewer Honors, Top-Cited and Top-Viewed Articles.

Authors: Sung Eun Song¹ sungeuns@korea.ac.kr

Source: Journal of the Korean Society of Radiology (2951-0805). Sep2025, Vol. 86 Issue 5, p697-703. 7p.

Acknowledgment to the Reviewers of Diagnostics in 2022.

Source: Diagnostics (2075-4418). Feb2023, Vol. 13 Issue 3, p353. 60p.

Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT.

Authors: Yeoh H Hong SH Ahn C et al.

Source: Korean journal of radiology [Korean J Radiol] 2021 Nov; Vol. 22 (11), pp. 1850-1857. Date of Electronic Publication: 2021 Aug 19.

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal Info: Publisher: Korean Society of Radiology Country of Publication: Korea (South) NLM ID: 100956096 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2005-8330 (Electronic) Linking ISSN: 12296929 NLM ISO Abbreviation: Korean J Radiol Subsets: MEDLINE

Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI.

Authors: Kim, Sihwan Park, Changmin Jeon, Gwanghyeon et al.

Source: Bioengineering (Basel). Jan2025, Vol. 12 Issue 1, p81. 17p.

Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.

Authors: Lee, Jemyoung Kim, Minbeom Park, Heejun et al.

Source: Bioengineering (Basel). Jan2025, Vol. 12 Issue 1, p64. 23p.

Performance of 1-mm non-gated low-dose chest computed tomography using deep learning-based noise reduction for coronary artery calcium scoring.

Authors: Choi, Hyewon Park, Eun-Ah Ahn, Chulkyun et al.

Source: European Radiology. Jun2023, Vol. 33 Issue 6, p3839-3847. 9p. 1 Black and White Photograph, 1 Diagram, 5 Charts, 1 Graph.

Subjects: Coronary artery calcification, Noise control, Intraclass correlation, Tomography, Computed tomography

AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography.

Authors: Ahn, Chulkyun Kim, Jong Hyo

Source: Diagnostics (2075-4418). Jan2024, Vol. 14 Issue 1, p96. 15p.

Deep learning–based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis.

Authors: Lee, Taehee Lee, Jeong Min Yoon, Jeong Hee et al.

Source: European Radiology. Sep2022, Vol. 32 Issue 9, p6407-6417. 11p. 3 Black and White Photographs, 5 Charts, 1 Graph.

Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions.

Authors: Park, Sungeun Yoon, Jeong Hee Joo, Ijin et al.

Source: European Radiology. May2022, Vol. 32 Issue 5, p2865-2874. 10p. 2 Black and White Photographs, 1 Diagram, 5 Charts, 1 Graph.

Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study.

Authors: Choi, Hyunsu Chang, Won Kim, Jong Hyo et al.

Source: European Radiology. Feb2022, Vol. 32 Issue 2, p1247-1255. 9p. 1 Color Photograph, 1 Black and White Photograph, 4 Charts, 3 Graphs.

Subjects: Image reconstruction algorithms, Reduction potential, Deep learning, Computed tomography, Rear-screen projection, Signal convolution

A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.

Authors: Kim, Youngwoo Hong, Byung Woo Kim, Seung Ja et al.

Source: Medical Physics. Jul2014, Vol. 41 Issue 7, p1-N.PAG. 16p.

Subjects: Mammograms, Estimation theory, Adipose tissues, Image segmentation, Image databases, Probability theory

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program.

Authors: Kim, Sihwan Jeong, Woo Kyoung Choi, Jin Hwa et al.

Source: PLoS ONE. 9/29/2022, Vol. 17 Issue 9, p1-15. 15p.

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.

Authors: Chun, Minsoo Choi, Jin Hwa Kim, Sihwan et al.

Source: PLoS ONE. 7/20/2022, Vol. 17 Issue 7, p1-18. 18p.

Emphysema quantification using low-dose computed tomography with deep learning–based kernel conversion comparison.

Authors: Bak, So Hyeon Kim, Jong Hyo Jin, Hyeongmin et al.

Source: European Radiology. Dec2020, Vol. 30 Issue 12, p6779-6787. 9p. 1 Color Photograph, 1 Diagram, 4 Charts, 2 Graphs.

Subjects: Deep learning, Lung diseases, Bland-Altman plot, Obstructive lung diseases