Sing Chun Lee

19 papers receiving 434 citations

Peers

Sing Chun Lee
Comparison fields: 5 of 63
  • Computer Vision and Pattern Recognition 146
  • Health Informatics 7
  • Biomedical Engineering 160
  • Surgery 137
  • Computer Graphics and Computer-Aided Design 12
Replace Konrad Leibrandt with:
Konrad Leibrandt United Kingdom
Martin Gröger Germany
Song Xu China
Joonho Seo South Korea
Anthony Meng Huat Tiong Singapore
Sandro Michael Heining Germany
Guowen Chen China
Piyamate Wisanuvej United Kingdom
Thomas Low United States
Linfei Xiong China
Sing Chun Lee relative to Konrad Leibrandt United Kingdom Konrad Leibrandt's profile →
Citations per field
00.5×
Konrad Leibrandt · 1×
Citations per year

Countries citing papers authored by Sing Chun Lee

Since Specialization
Citations

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

Fields of papers citing papers by Sing Chun Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sing Chun Lee. 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 Sing Chun Lee. The network helps show where Sing Chun Lee may publish in the future.

Co-authors

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

All Works

19 of 19 papers shown
#Work
1 201679
2 201647
3 201647
4 201744
5 201941
6 200330
7 201629
8 201926
9 201718
10 201815
11 201814
12 201911
13 20209
14 20178
15 20187
16 20197
17 20235
18 20171
19 20181

About Sing Chun Lee

Sing Chun Lee is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Surgery, Computational Mechanics and Aerospace Engineering, having authored 19 papers that have together received 439 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (4 papers), Augmented Reality Applications (3 papers), Robotics and Sensor-Based Localization (3 papers), Surgical Simulation and Training (2 papers), Anatomy and Medical Technology (2 papers), Advanced X-ray and CT Imaging (2 papers), 3D Shape Modeling and Analysis (2 papers) and Organic Electronics and Photovoltaics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (146 citations), Health Informatics (7 citations), Biomedical Engineering (160 citations), Surgery (137 citations) and Computer Graphics and Computer-Aided Design (12 citations). Sing Chun Lee has collaborated with scholars based in United States, Germany and Hong Kong. Frequent co-authors include Nassir Navab, Javad Fotouhi, Bernhard Fuerst, Greg Osgood, Russell H. Taylor, Alex Johnson, Mathias Unberath, Mehran Armand, Gregory D. Hager and Marius Fischer. Their work appears in journals such as International Journal of Computer Assisted Radiology and Surgery, Computer Graphics Forum, Medical Physics, Chemistry - A European Journal and IEEE Robotics and Automation Letters.

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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