Sanghoon Jun

1.9k citations
27 papers · 1.2k · 1 hit paper · h-index 10

Impact in

Papers in

Sanghoon Jun

25 papers receiving 1.2k citations

Sanghoon Jun's Hit Papers

Deep Learning in Medical Imaging: General Overview 2017 · 848 citations
8480+3+6Years since publication250500750

Peers

Sanghoon Jun
Comparison fields: 5 of 136
  • Health Informatics 104
  • Radiology, Nuclear Medicine and Imaging 487
  • Signal Processing 122
  • Artificial Intelligence 354
  • Computer Vision and Pattern Recognition 201
Replace Veronika Cheplygina with:
Veronika Cheplygina Netherlands
Marcos Ortega Spain
Miguel Á. González Ballester Spain
Fırat Hardalaç Türkiye
Sara Moccia Italy
Idit Diamant Israel
Narendra D. Londhe India
Muhammad Owais Pakistan
S. M. Reza Soroushmehr United States
Tao Tan China
Sanghoon Jun relative to Veronika Cheplygina Netherlands Veronika Cheplygina's profile →
Citations per field
00.5×2.6×
Veronika Cheplygina · 1×
Citations per year

Countries citing papers authored by Sanghoon Jun

Since Specialization
Citations

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

Fields of papers citing papers by Sanghoon Jun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Sanghoon Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sanghoon Jun Line = papers co-authored together Sanghoon Jun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Learning in Medical Imaging: General Overview
Hit paper breakdown →
2017848
2 200995
3 201781
4 201756
5 200825
6 202115
7 201714
8 201512
9 202311
10 201010
11 20238
12 20228
13 20217
14 20146
15 20136
16 20175
17 20165
18 20254
19 20094
20 20134

About Sanghoon Jun

Sanghoon Jun is a scholar working on Civil and Structural Engineering, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 27 papers that have together received 1.2k indexed citations. Recurring topics across this work include Water Systems and Optimization (9 papers), Music and Audio Processing (9 papers), Music Technology and Sound Studies (7 papers), Speech and Audio Processing (5 papers), COVID-19 diagnosis using AI (3 papers), Water Quality Monitoring Technologies (3 papers), Infrastructure Maintenance and Monitoring (3 papers) and Geotechnical Engineering and Underground Structures (3 papers). The work is most often cited by research in Health Informatics (104 citations), Radiology, Nuclear Medicine and Imaging (487 citations), Signal Processing (122 citations), Artificial Intelligence (354 citations) and Computer Vision and Pattern Recognition (201 citations). Sanghoon Jun has collaborated with scholars based in South Korea, United States and Türkiye. Frequent co-authors include Joon Beom Seo, Namkug Kim, Guk Bae Kim, Hyunna Lee, June‐Goo Lee, Eenjun Hwang, Seungmin Rho, Kevin Lansey, Jihoon Moon and David A. Lynch. Their work appears in journals such as Journal of Water Resources Planning and Management, Journal of Digital Imaging, Multimedia Tools and Applications, Water Research X and Water Resources Management.

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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