Seokmin Han

1.3k total citations
29 papers, 1000 citations indexed

About

Seokmin Han is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Seokmin Han has authored 29 papers receiving a total of 1000 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Pulmonary and Respiratory Medicine and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Seokmin Han's work include Advanced X-ray and CT Imaging (7 papers), Medical Imaging Techniques and Applications (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Seokmin Han is often cited by papers focused on Advanced X-ray and CT Imaging (7 papers), Medical Imaging Techniques and Applications (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Seokmin Han collaborates with scholars based in South Korea, Japan and United States. Seokmin Han's co-authors include Seung‐Koo Lee, Ji Hoe Heo, Moon Ho Park, Wonsik Kim, Ja-Yeon Jeong, Won-Chul Bang, Sung Il Hwang, Hak Jong Lee, Masahiro Yamaguchi and Sangmin Lee and has published in prestigious journals such as Journal of Applied Physics, Free Radical Biology and Medicine and Expert Systems with Applications.

In The Last Decade

Seokmin Han

27 papers receiving 946 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seokmin Han South Korea 13 341 278 248 155 142 29 1000
Lalit Mohan Aggarwal India 11 277 0.8× 169 0.6× 277 1.1× 126 0.8× 92 0.6× 45 878
Muhammad Aksam Iftikhar Pakistan 19 471 1.4× 386 1.4× 327 1.3× 263 1.7× 93 0.7× 55 1.5k
Ioannis Kalatzis Greece 19 356 1.0× 294 1.1× 193 0.8× 131 0.8× 90 0.6× 73 946
Mayun Chen China 20 151 0.4× 230 0.8× 151 0.6× 66 0.4× 247 1.7× 34 1.3k
Kelvin Wong United States 26 567 1.7× 79 0.3× 217 0.9× 109 0.7× 136 1.0× 78 1.9k
Joshua Cates United States 20 355 1.0× 259 0.9× 300 1.2× 57 0.4× 46 0.3× 55 1.5k
Saima Rathore United States 19 796 2.3× 416 1.5× 181 0.7× 324 2.1× 148 1.0× 54 1.4k
Mingxia Liu China 19 377 1.1× 430 1.5× 348 1.4× 452 2.9× 57 0.4× 44 1.6k

Countries citing papers authored by Seokmin Han

Since Specialization
Citations

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

Fields of papers citing papers by Seokmin Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seokmin Han

This figure shows the co-authorship network connecting the top 25 collaborators of Seokmin Han. A scholar is included among the top collaborators of Seokmin Han 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 Seokmin Han. Seokmin Han 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.
Kang, Mingu, et al.. (2021). Resilience against Adversarial Examples: Data-Augmentation Exploiting Generative Adversarial Networks. KSII Transactions on Internet and Information Systems. 15(11). 8 indexed citations
2.
Han, Seokmin, Sung Il Hwang, & Hak Jong Lee. (2020). A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images. Journal of Digital Imaging. 33(4). 838–845. 4 indexed citations
3.
Han, Seokmin, et al.. (2020). Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network. KSII Transactions on Internet and Information Systems. 15 indexed citations
4.
Han, Seokmin, et al.. (2019). A Practical Implementation of Deep Learning Method for Supporting the Classification of Breast Lesions in Ultrasound Images. International journal of advanced smart convergence. 8(1). 24–34. 2 indexed citations
5.
Han, Seokmin, Sung Il Hwang, & Hak Jong Lee. (2019). The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method. Journal of Digital Imaging. 32(4). 638–643. 82 indexed citations
6.
Han, Seokmin, et al.. (2018). Visualization of Malwares for Classification Through Deep Learning. Journal of Internet Computing and services. 19(5). 67–75. 1 indexed citations
7.
Han, Seokmin, et al.. (2017). A deep learning framework for supporting the classification of breast lesions in ultrasound images. Physics in Medicine and Biology. 62(19). 7714–7728. 269 indexed citations
8.
Han, Seokmin, et al.. (2016). A distance-driven deconvolution method for CT image-resolution improvement. Journal of the Korean Physical Society. 69(12). 1830–1833.
9.
Han, Seokmin. (2015). A Quantification Method for Breast Tissue Thickness and Iodine Concentration Using Photon-Counting Detector. Journal of Digital Imaging. 28(5). 594–603. 2 indexed citations
10.
Han, Seokmin, et al.. (2013). Tissue Cancellation in Dual Energy Mammography Using a Calibration Phantom Customized for Direct Mapping. IEEE Transactions on Medical Imaging. 33(1). 74–84. 2 indexed citations
11.
Han, Seokmin, et al.. (2012). Ultrasensitive determination of epicatechin, rutin, and quercetin by capillary electrophoresis chemiluminescence. Acta Chromatographica. 24(4). 679–688. 27 indexed citations
12.
13.
Han, Seokmin, et al.. (2006). Quantitative characterization of degradation behaviors of antioxidants stabilized in lipid particles. Talanta. 71(5). 2129–2133. 8 indexed citations
14.
Han, Seokmin, et al.. (2006). A non-integrated type noise suppressor incorporated with a highly resistive Co–Fe–Al–O thin film on a coplanar waveguide transmission line. Journal of Magnetism and Magnetic Materials. 311(2). 708–713. 3 indexed citations
15.
Han, Seokmin, et al.. (2006). Electromagnetic noise suppression characteristics of a coplanar waveguide transmission line integrated with a magnetic thin film. Journal of Applied Physics. 100(12). 6 indexed citations
16.
Heo, Ji Hoe, Seokmin Han, & Seung‐Koo Lee. (2005). Free radicals as triggers of brain edema formation after stroke. Free Radical Biology and Medicine. 39(1). 51–70. 257 indexed citations
17.
Han, Seokmin. (2004). The relationship between aluminum and spontaneous pneumothorax; treatment, prognosis, follow-up?. Interactive Cardiovascular and Thoracic Surgery. 3(1). 79–82. 12 indexed citations
18.
Kim, Jongrae, et al.. (2004). Nanocrystalline Fe–Co–Ni–B thin film with high permeability and high-frequency characteristics. Journal of Magnetism and Magnetic Materials. 290-291. 205–208. 38 indexed citations
19.
Han, Seokmin. (2003). Transaxillary approach in thoracic outlet syndrome: the importance of resection of the first-rib. European Journal of Cardio-Thoracic Surgery. 24(3). 428–433. 22 indexed citations
20.
Ryu, Ho Jin, et al.. (1995). Soft magnetic properties of Fe-TM-C-N (TM:Zr,Nb) nanocrystalline films. IEEE Transactions on Magnetics. 31(6). 3868–3870. 5 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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