Yohan Jun

1.5k citations
23 papers · 605 · 1 hit paper · h-index 10

Impact in

Papers in

Yohan Jun

19 papers receiving 598 citations

Yohan Jun's Hit Papers

KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images 2018 · 312 citations
3120+2+5Years since publication100200300

Peers

Yohan Jun
Comparison fields: 5 of 54
  • Radiology, Nuclear Medicine and Imaging 419
  • Health Informatics 19
  • Neurology 33
  • Computational Mechanics 78
  • Computer Vision and Pattern Recognition 74
Replace Taejoon Eo with:
Taejoon Eo South Korea
Jinseong Jang South Korea
Salman Ul Hassan Dar Türkiye
Shun Zhu China
Christopher M. Sandino United States
Taohui Xiao China
Manav Bhushan United Kingdom
Xinyuan Zhang China
Itthi Chatnuntawech Thailand
Yohan Jun relative to Taejoon Eo South Korea Taejoon Eo's profile →
Citations per field
00.5×1.5×
Taejoon Eo · 1×
Citations per year

Countries citing papers authored by Yohan Jun

Since Specialization
Citations

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

Fields of papers citing papers by Yohan Jun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yohan 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 Yohan Jun Line = papers co-authored together Yohan Jun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1
KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images
Hit paper breakdown →
2018312
2 202143
3 202136
4 201835
5 202030
6 202129
7 201924
8 202322
9 201921
10 202312
11 20239
12 20249
13 20237
14 20247
15 20205
16 20251
17 20161
18 20241
19 20251
20 20250

About Yohan Jun

Yohan Jun is a scholar working on Radiology, Nuclear Medicine and Imaging, Cellular and Molecular Neuroscience, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics, having authored 23 papers that have together received 605 indexed citations. Recurring topics across this work include Advanced MRI Techniques and Applications (13 papers), Medical Imaging Techniques and Applications (6 papers), Advanced Neuroimaging Techniques and Applications (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), COVID-19 diagnosis using AI (2 papers), Neuroscience and Neural Engineering (1 paper), Advanced Radiotherapy Techniques (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (419 citations), Health Informatics (19 citations), Neurology (33 citations), Computational Mechanics (78 citations) and Computer Vision and Pattern Recognition (74 citations). Yohan Jun has collaborated with scholars based in South Korea, United States and Germany. Frequent co-authors include Dosik Hwang, Taejoon Eo, Taeseong Kim, Ho‐Joon Lee, Jinseong Jang, Hyungseob Shin, Sung Soo Ahn, Yae Won Park, Kyunghwa Han and Chansik An. Their work appears in journals such as Magnetic Resonance in Medicine, European Radiology, Medical Image Analysis, Nature Communications and Investigative Radiology.

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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