Kun Wu

736 citations
17 papers · 527 · h-index 9

Impact in

Papers in

Kun Wu

16 papers receiving 510 citations

Peers

Kun Wu
Comparison fields: 5 of 106
  • Health Informatics 9
  • Pathology and Forensic Medicine 90
  • Oncology 125
  • Public Health, Environmental and Occupational Health 106
  • Orthopedics and Sports Medicine 23
Replace Xiaoming Xu with:
Xiaoming Xu China
Charalampos Chrelias Greece
Minfang Tao China
Elena Guerra Italy
Yul Hwangbo South Korea
Takaaki Sato Japan
Nuthar Jassam United Kingdom
Ziyi Li China
Jeong Yoon Yim South Korea
Elisabeth Couto Norway
Kun Wu relative to Xiaoming Xu China Xiaoming Xu's profile →
Citations per field
00.5×7.3×
Xiaoming Xu · 1×
Citations per year

Countries citing papers authored by Kun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Kun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 2002258
2 200579
3 202053
4 201141
5 202119
6 201118
7 202315
8 201214
9 201910
10 20226
11 20194
12 20094
13 20233
14 20251
15 20241
16
Acquired immunodeficiency syndrome and Cryptosporidium infection.
19941
17
Facial Expression Recognition Using Embedded
20090

About Kun Wu

Kun Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Infectious Diseases and Molecular Biology, having authored 17 papers that have together received 527 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (3 papers), Face and Expression Recognition (2 papers), Image and Signal Denoising Methods (2 papers), Advanced Image Fusion Techniques (2 papers), Artificial Intelligence in Games (1 paper), Consumer Market Behavior and Pricing (1 paper), Diet and metabolism studies (1 paper) and Image Enhancement Techniques (1 paper). The work is most often cited by research in Health Informatics (9 citations), Pathology and Forensic Medicine (90 citations), Oncology (125 citations), Public Health, Environmental and Occupational Health (106 citations) and Orthopedics and Sports Medicine (23 citations). Kun Wu has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Edward L. Giovannucci, F. B. Hu, W. C. Willett, Charles S. Fuchs, Graham A. Colditz, Halcyon G. Skinner, Dominique S. Michaud, Wenkai Yang, Yunyun Dong and Qianqian Du. Their work appears in journals such as American Journal of Epidemiology, JNCI Journal of the National Cancer Institute, HIV Clinical Trials, Scientific Reports and The Visual Computer.

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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