Hackjoon Shim

695 total citations
50 papers, 499 citations indexed

About

Hackjoon Shim is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hackjoon Shim has authored 50 papers receiving a total of 499 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 25 papers in Biomedical Engineering and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hackjoon Shim's work include Advanced X-ray and CT Imaging (17 papers), Medical Image Segmentation Techniques (16 papers) and Radiation Dose and Imaging (12 papers). Hackjoon Shim is often cited by papers focused on Advanced X-ray and CT Imaging (17 papers), Medical Image Segmentation Techniques (16 papers) and Radiation Dose and Imaging (12 papers). Hackjoon Shim collaborates with scholars based in South Korea, United States and Ethiopia. Hackjoon Shim's co-authors include Il Dong Yun, Cheng Tao, C. Kent Kwoh, Kyongtae T. Bae, Samuel Chang, Hyuk‐Jae Chang, Sang Uk Lee, Yeonggul Jang, Jin Wook Chung and Roh‐Eul Yoo and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Hackjoon Shim

44 papers receiving 489 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hackjoon Shim South Korea 12 269 197 150 103 78 50 499
Gengyan Zhao United States 8 357 1.3× 273 1.4× 84 0.6× 147 1.4× 20 0.3× 9 645
Hiroyuki Sugimori Japan 14 356 1.3× 147 0.7× 56 0.4× 37 0.4× 126 1.6× 93 767
Kevin Leung United States 8 195 0.7× 106 0.5× 28 0.2× 90 0.9× 51 0.7× 17 389
Jiahong Ouyang United States 14 309 1.1× 136 0.7× 86 0.6× 49 0.5× 104 1.3× 34 626
Ruida Cheng United States 9 106 0.4× 143 0.7× 94 0.6× 28 0.3× 95 1.2× 14 309
Bryon R. Gomberg United States 16 451 1.7× 152 0.8× 172 1.1× 63 0.6× 31 0.4× 19 1.2k
Gian Marco Conte United States 15 413 1.5× 93 0.5× 90 0.6× 12 0.1× 51 0.7× 35 691
Karl Fritscher Austria 16 407 1.5× 325 1.6× 220 1.5× 15 0.1× 128 1.6× 49 890
David G. Gobbi Canada 11 229 0.9× 179 0.9× 206 1.4× 11 0.1× 38 0.5× 44 569

Countries citing papers authored by Hackjoon Shim

Since Specialization
Citations

This map shows the geographic impact of Hackjoon Shim'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 Hackjoon Shim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hackjoon Shim more than expected).

Fields of papers citing papers by Hackjoon Shim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hackjoon Shim. 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 Hackjoon Shim. The network helps show where Hackjoon Shim may publish in the future.

Co-authorship network of co-authors of Hackjoon Shim

This figure shows the co-authorship network connecting the top 25 collaborators of Hackjoon Shim. A scholar is included among the top collaborators of Hackjoon Shim 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 Hackjoon Shim. Hackjoon Shim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kim, Hyunjung, et al.. (2024). A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms. British Journal of Radiology. 97(1160). 1492–1500. 2 indexed citations
2.
Hong, Youngtaek, et al.. (2024). Fully Convolutional Hybrid Fusion Network With Heterogeneous Representations for Identification of S1 and S2 From Phonocardiogram. IEEE Journal of Biomedical and Health Informatics. 28(12). 7151–7163. 2 indexed citations
3.
4.
Shim, Hackjoon, et al.. (2023). Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies. Diagnostics. 13(11). 1862–1862. 2 indexed citations
5.
Shim, Hackjoon, et al.. (2023). The effectiveness of post-processing head and neck CT angiography using contrast enhancement boost technique. PLoS ONE. 18(4). e0284793–e0284793. 8 indexed citations
6.
Woo, Ji Young, et al.. (2022). Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction. Korean Journal of Radiology. 23(11). 1044–1044. 24 indexed citations
7.
Jang, Yeonggul, et al.. (2020). Generation of High-Resolution Chest X-rays using Multi-scale Conditional Generative Adversarial Network with Attention. Journal of Broadcast Engineering. 25(1). 1–12. 1 indexed citations
8.
9.
Jang, Yeonggul, et al.. (2016). Generation of Triangular Mesh of Coronary Artery Using Mesh Merging. Journal of KIISE. 43(4). 419–429.
10.
11.
Kwak, Kichang, Uicheul Yoon, Dong-Kyun Lee, et al.. (2013). Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening. Magnetic Resonance Imaging. 31(7). 1190–1196. 40 indexed citations
12.
Yoo, Roh‐Eul, Eun‐Ah Park, Whal Lee, et al.. (2012). Image quality of Adaptive Iterative Dose Reduction 3D of coronary CT angiography of 640-slice CT: comparison with filtered back-projection. International journal of cardiac imaging. 29(3). 669–676. 57 indexed citations
13.
Kim, Kil Joong, Kyoung Ho Lee, Bohyoung Kim, et al.. (2011). JPEG2000 2D and 3D Reversible Compressions of Thin-Section Chest CT Images: Improving Compressibility by Increasing Data Redundancy Outside the Body Region. Radiology. 259(1). 271–277. 11 indexed citations
14.
Lee, Soochahn, et al.. (2011). Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images. Computer Vision and Image Understanding. 115(12). 1710–1720. 29 indexed citations
15.
Bae, K.T., Hackjoon Shim, Cheng Tao, et al.. (2009). Intra- and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method. Osteoarthritis and Cartilage. 17(12). 1589–1597. 38 indexed citations
16.
17.
Shim, Hackjoon, et al.. (2009). Semiautomated Segmentation of Kidney From High-Resolution Multidetector Computed Tomography Images Using a Graph-Cuts Technique. Journal of Computer Assisted Tomography. 33(6). 893–901. 19 indexed citations
18.
Lee, Soochahn, Hackjoon Shim, Il Dong Yun, et al.. (2009). Fully automatic 3-D segmentation of knee bone compartments by iterative local branch-and-mincut on MR images from osteoarthritis initiative (OAI). 23. 3381–3384. 4 indexed citations
19.
Shim, Hackjoon, et al.. (2006). Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering. Computer Methods and Programs in Biomedicine. 84(2-3). 135–145. 23 indexed citations
20.
Shim, Hackjoon, et al.. (2005). Utilization of an alternative Communication Device using the Anal Sphincter (CDAS). PubMed. 248. 6817–6820. 2 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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