Hong‐Jun Yoon
Impact in
- Health Informatics top 2%
- Artificial Intelligence top 2%
- Topic Modeling
- AI in cancer detection
- Machine Learning in Healthcare
- Natural Language Processing Techniques
Papers in
-
- AI in cancer detection 21
- Topic Modeling 18
- Natural Language Processing Techniques 6
- Machine Learning in Healthcare 6
- Co-authors
- Georgia D. TourassiJohn X. QiuPaul FearnShang GaoMohammed AlawadL. KojovicD.C. YuXiao‐Cheng Wu
- Journals
- IEEE Journal of Biomedical and Health Informatics (3 papers)Journal of Biomedical Informatics (3 papers)Medical Physics (2 papers)Academic Radiology (2 papers)BMC Bioinformatics (2 papers)
- Partner nations
- United StatesSouth KoreaBelgium
In The Last Decade
Hong‐Jun Yoon
63 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 128
- Health Informatics 55
- Artificial Intelligence 586
- Health Information Management 61
- Radiology, Nuclear Medicine and Imaging 244
- Control and Systems Engineering 86
Countries citing papers authored by Hong‐Jun Yoon
This map shows the geographic impact of Hong‐Jun Yoon'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 Hong‐Jun Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hong‐Jun Yoon more than expected).
Fields of papers citing papers by Hong‐Jun Yoon
This network shows the impact of papers produced by Hong‐Jun Yoon. 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 Hong‐Jun Yoon. The network helps show where Hong‐Jun Yoon may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hong‐Jun Yoon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 16 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 0 | |
| 6 | 2021 | 37 | |
| 7 | 2021 | 25 | |
| 8 | 2020 | 31 | |
| 9 | 2019 | 15 | |
| 10 | 2019 | 40 | |
| 11 | 2018 | 0 | |
| 12 | 2018 | 10 | |
| 13 | 2017 | 89 | |
| 14 | 2015 | 5 | |
| 15 | 2014 | 12 | |
| 16 | 2014 | 24 | |
| 17 | 2012 | 9 | |
| 18 | 2012 | 0 | |
| 19 | Resistance Factors for Drilled Shafts Embedded in Weathered Rock | 2007 | 0 |
| 20 | 2006 | 25 |
About Hong‐Jun Yoon
Hong‐Jun Yoon is a scholar working on Artificial Intelligence, Health Informatics, Human-Computer Interaction, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 68 papers that have together received 1.0k indexed citations. Recurring topics across this work include AI in cancer detection (21 papers), Topic Modeling (18 papers), Radiomics and Machine Learning in Medical Imaging (11 papers), Biomedical Text Mining and Ontologies (11 papers), Natural Language Processing Techniques (6 papers), Machine Learning in Healthcare (6 papers), Medical Image Segmentation Techniques (4 papers) and Image Retrieval and Classification Techniques (4 papers). The work is most often cited by research in Health Informatics (55 citations), Artificial Intelligence (586 citations), Health Information Management (61 citations), Radiology, Nuclear Medicine and Imaging (244 citations) and Control and Systems Engineering (86 citations). Hong‐Jun Yoon has collaborated with scholars based in United States, South Korea and Belgium. Frequent co-authors include Georgia D. Tourassi, John X. Qiu, Paul Fearn, Shang Gao, Mohammed Alawad, L. Kojovic, D.C. Yu, Xiao‐Cheng Wu, Linda Coyle and Songhua Xu. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, Journal of Biomedical Informatics, Medical Physics, Academic Radiology and BMC Bioinformatics.
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.