Yee Kai Tee

1.3k citations
54 papers · 884 · h-index 17

Impact in

Papers in

Yee Kai Tee

51 papers receiving 869 citations

Peers

Yee Kai Tee
Comparison fields: 5 of 105
  • Biophysics 125
  • Radiology, Nuclear Medicine and Imaging 395
  • Materials Chemistry 415
  • Health Informatics 10
  • Rheumatology 62
Replace Xing Lü with:
Xing Lü China
Cem M. Deniz United States
Jong Hwi Jeong South Korea
Andrew Kalisz United States
Xulei Qin United States
Segyeong Joo South Korea
Fengjun Zhao China
Eugene Ozhinsky United States
Spiros Kostopoulos Greece
Xiaowei Ding China
Yee Kai Tee relative to Xing Lü China Xing Lü's profile →
Citations per field
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Xing Lü · 1×
Citations per year

Countries citing papers authored by Yee Kai Tee

Since Specialization
Citations

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

Fields of papers citing papers by Yee Kai Tee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yee Kai Tee. 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 Yee Kai Tee. The network helps show where Yee Kai Tee may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014145
2 201487
3 202186
4 201949
5 201246
6 202143
7 202231
8 201327
9 201227
10 202226
11 202326
12 202125
13 202122
14 201320
15 202318
16 202318
17 202016
18 202316
19 201916
20 202113

About Yee Kai Tee

Yee Kai Tee is a scholar working on Radiology, Nuclear Medicine and Imaging, Materials Chemistry, Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 54 papers that have together received 884 indexed citations. Recurring topics across this work include Lanthanide and Transition Metal Complexes (18 papers), Advanced MRI Techniques and Applications (13 papers), AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (8 papers), Electron Spin Resonance Studies (7 papers), Speech and Audio Processing (6 papers), Colorectal Cancer Screening and Detection (5 papers) and MRI in cancer diagnosis (3 papers). The work is most often cited by research in Biophysics (125 citations), Radiology, Nuclear Medicine and Imaging (395 citations), Materials Chemistry (415 citations), Health Informatics (10 citations) and Rheumatology (62 citations). Yee Kai Tee has collaborated with scholars based in Malaysia, United Kingdom and China. Frequent co-authors include Yan Chai Hum, Michael A. Chappell, Khin Wee Lai, Stephen J. Payne, George Harston, Wun‐She Yap, Peter Jezzard, Fintan Sheerin, James Kennedy and Thomas W. Okell. Their work appears in journals such as Magnetic Resonance in Medicine, Multimedia Tools and Applications, NMR in Biomedicine, IEEE Access and Computational Intelligence and Neuroscience.

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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