Dong‐Hyun Kim

1.6k total citations · 1 hit paper
81 papers, 1.2k citations indexed

About

Dong‐Hyun Kim is a scholar working on Radiology, Nuclear Medicine and Imaging, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. According to data from OpenAlex, Dong‐Hyun Kim has authored 81 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Atomic and Molecular Physics, and Optics and 15 papers in Electrical and Electronic Engineering. Recurrent topics in Dong‐Hyun Kim's work include Advanced MRI Techniques and Applications (51 papers), Atomic and Subatomic Physics Research (19 papers) and Advanced Neuroimaging Techniques and Applications (15 papers). Dong‐Hyun Kim is often cited by papers focused on Advanced MRI Techniques and Applications (51 papers), Atomic and Subatomic Physics Research (19 papers) and Advanced Neuroimaging Techniques and Applications (15 papers). Dong‐Hyun Kim collaborates with scholars based in South Korea, United States and Netherlands. Dong‐Hyun Kim's co-authors include Mohammed A. Al‐masni, Tae‐Seong Kim, Kwan Woo Lee, Xiaoling Li, Sung‐E Choi, Ting Fu, Jongsook Kim Kemper, Byron Kemper, Yup Kang and Sunmi Seok and has published in prestigious journals such as NeuroImage, Scientific Reports and Magnetic Resonance in Medicine.

In The Last Decade

Dong‐Hyun Kim

76 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification

2020 270 citations Mohammed A. Al‐masni, Dong‐Hyun Kim et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dong‐Hyun Kim South Korea	16	480	257	201	189	172	81	1.2k
Xiao Han China	25	660 1.4×	251 1.0×	122 0.6×	738 3.9×	236 1.4×	145	1.9k
Ken Chang United States	23	1.4k 3.0×	117 0.5×	116 0.6×	521 2.8×	69 0.4×	51	2.2k
Haruki Sekiguchi Japan	20	219 0.5×	139 0.5×	81 0.4×	33 0.2×	158 0.9×	90	1.7k
Dong Liang China	24	1.2k 2.6×	77 0.3×	53 0.3×	285 1.5×	42 0.2×	145	1.9k
Sung Soo Ahn South Korea	35	2.3k 4.7×	230 0.9×	742 3.7×	148 0.8×	57 0.3×	173	4.1k
Yongchang Zheng China	21	147 0.3×	330 1.3×	104 0.5×	54 0.3×	67 0.4×	78	1.4k
Melcior Sentís Spain	19	757 1.6×	171 0.7×	129 0.6×	729 3.9×	35 0.2×	61	1.6k
Ke Sheng United States	40	2.7k 5.6×	249 1.0×	88 0.4×	109 0.6×	83 0.5×	276	5.5k
Elisabeth Weiss United States	30	1.7k 3.5×	209 0.8×	114 0.6×	104 0.6×	18 0.1×	148	2.8k
Pinaki Sarder United States	16	211 0.4×	91 0.4×	48 0.2×	276 1.5×	58 0.3×	81	1.3k

Countries citing papers authored by Dong‐Hyun Kim

Since Specialization

Citations

This map shows the geographic impact of Dong‐Hyun Kim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dong‐Hyun Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong‐Hyun Kim more than expected).

Fields of papers citing papers by Dong‐Hyun Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong‐Hyun Kim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dong‐Hyun Kim. The network helps show where Dong‐Hyun Kim may publish in the future.

Co-authorship network of co-authors of Dong‐Hyun Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Dong‐Hyun Kim. A scholar is included among the top collaborators of Dong‐Hyun Kim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dong‐Hyun Kim. Dong‐Hyun Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Mandija, Stefano, Cornelis A. T. van den Berg, Y.Z. Ider, et al.. (2025). MR Electrical Properties Tomography Acquisitions: A Guideline From the ISMRM Electro‐Magnetic Tissue Properties Study Group. Journal of Magnetic Resonance Imaging. 63(1). 279–281. 1 indexed citations

Al‐masni, Mohammed A., Seul Lee, Sun Young Jung, et al.. (2025). Unsupervised learning for motion correction and assessment in brain magnetic resonance imaging using severity-based regularized cycle consistency. Engineering Applications of Artificial Intelligence. 142. 109978–109978.

Arduino, Alessandro, Cornelis A. T. van den Berg, Dong‐Hyun Kim, et al.. (2025). Construction of Phantoms for MR Electrical Properties Tomography (From Structure to Composition): A Guideline From the ISMRM Electro‐Magnetic Tissue Properties Study Group. Journal of Magnetic Resonance Imaging. 63(1). 282–285. 1 indexed citations

Kim, Dong‐Hyun, et al.. (2024). Detailed three-dimensional analyses of tyloses in oak used for bourbon and wine barrels through X-ray computed tomography. Scientific Reports. 14(1). 17044–17044. 1 indexed citations

Kim, Dong‐Hyun, et al.. (2024). Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds. Acta Neurochirurgica. 166(1). 381–381. 3 indexed citations

Al‐masni, Mohammed A., et al.. (2023). A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI. Computers in Biology and Medicine. 153. 106553–106553. 5 indexed citations

Liu, Lisheng, Chia‐Wei Lee, Dong‐Hyun Kim, et al.. (2023). Augmented Reality Surgical Navigation System Integrated with Deep Learning. Bioengineering. 10(5). 617–617. 11 indexed citations

Mandija, Stefano, et al.. (2023). Data‐driven electrical conductivity brain imaging using 3 T MRI. Human Brain Mapping. 44(15). 4986–5001. 8 indexed citations

Kim, Jun‐Ho, Seul Lee, Young Noh, et al.. (2022). Detection of Cerebral Microbleeds in MR Images Using a Single‐Stage Triplanar Ensemble Detection Network (TPE‐Det). Journal of Magnetic Resonance Imaging. 58(1). 272–283. 6 indexed citations

10.

Cho, Yejin, et al.. (2022). Myelin Water Imaging of Nerve Recovery in Rehabilitating Stroke Patients. Journal of Magnetic Resonance Imaging. 56(5). 1548–1556. 3 indexed citations

11.

Al‐masni, Mohammed A., et al.. (2021). 3D Multi-Scale Residual Network Toward Lacunar Infarcts Identification From MR Images With Minimal User Intervention. IEEE Access. 9. 11787–11797. 7 indexed citations

12.

Kim, Dong‐Hyun, et al.. (2021). Improved Multi-Echo Gradient-Echo-Based Myelin Water Fraction Mapping Using Dimensionality Reduction. IEEE Transactions on Medical Imaging. 41(1). 27–38. 3 indexed citations

13.

Lee, Seul, et al.. (2020). Deep Learning in MR Motion Correction: a Brief Review and a New Motion Simulation Tool (view2Dmotion). Investigative Magnetic Resonance Imaging. 24(4). 196–196. 29 indexed citations

14.

Ryu, Kanghyun, et al.. (2020). Artificial neural network for multi‐echo gradient echo–based myelin water fraction estimation. Magnetic Resonance in Medicine. 85(1). 380–389. 15 indexed citations

15.

Shin, Jaewook, et al.. (2020). Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging. IEEE Transactions on Medical Imaging. 39(6). 2235–2245. 10 indexed citations

16.

Kim, Dong‐Hyun, et al.. (2019). Gastric Lesion Classification Using Deep Learning Based on Fast and Robust Fuzzy C-Means and Simple Linear Iterative Clustering Superpixel Algorithms. Journal of Electrical Engineering and Technology. 14(6). 2549–2556. 11 indexed citations

17.

Kim, Dong‐Hyun & Hyun‐chong Cho. (2018). Deep Learning based Computer-aided Diagnosis System for Gastric Lesion using Endoscope. The Transactions of The Korean Institute of Electrical Engineers. 67(7). 928–933. 3 indexed citations

18.

Kim, Dong‐Hyun, et al.. (2013). Simultaneous imaging of in vivo conductivity and susceptibility. Magnetic Resonance in Medicine. 71(3). 1144–1150. 36 indexed citations

19.

Kim, Dong‐Hyun, et al.. (2012). 3D imaging using magnetic resonance tomosynthesis (MRT) technique. Medical Physics. 39(8). 4733–4741. 1 indexed citations

20.

Park, Chanjong, et al.. (1998). A cell-based shared virtual world management mechanism in the cyber mall system. Computer Networks and ISDN Systems. 30(20-21). 1865–1874. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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