Muyi Sun
Impact in
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- Digital Imaging for Blood Diseases
- Generative Adversarial Networks and Image Synthesis
- Face recognition and analysis
- Ophthalmology top 5%
Papers in
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- Advanced Neural Network Applications 8
- Generative Adversarial Networks and Image Synthesis 7
- Face recognition and analysis 5
- Human Pose and Action Recognition 4
- Digital Imaging for Blood Diseases 3
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- Retinal Imaging and Analysis 5
- Radiomics and Machine Learning in Medical Imaging 4
- Co-authors
- Xiaoguang Zhou (14 shared papers)Xingqun Qi (17 shared papers)Hao Dang (10 shared papers)Qing Chang (4 shared papers)Zhenan Sun (8 shared papers)Yiwen Luo (2 shared papers)Qi Li (5 shared papers)Pengkun Liu (1 shared paper)
In The Last Decade
Muyi Sun
36 papers receiving 563 citations
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 230
- Ophthalmology 69
- Radiology, Nuclear Medicine and Imaging 179
- Cardiology and Cardiovascular Medicine 113
- Media Technology 38
Countries citing papers authored by Muyi Sun
This map shows the geographic impact of Muyi Sun'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 Muyi Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muyi Sun more than expected).
Fields of papers citing papers by Muyi Sun
This network shows the impact of papers produced by Muyi Sun. 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 Muyi Sun. The network helps show where Muyi Sun may publish in the future.
Co-authors
The 25 scholars most cited alongside Muyi Sun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 76 | |
| 2 | 2020 | 74 | |
| 3 | 2019 | 51 | |
| 4 | 2020 | 40 | |
| 5 | 2022 | 34 | |
| 6 | 2018 | 29 | |
| 7 | 2019 | 28 | |
| 8 | 2017 | 23 | |
| 9 | 2022 | 21 | |
| 10 | 2020 | 21 | |
| 11 | 2018 | 19 | |
| 12 | 2021 | 18 | |
| 13 | 2022 | 14 | |
| 14 | 2021 | 12 | |
| 15 | 2022 | 11 | |
| 16 | 2019 | 10 | |
| 17 | 2023 | 10 | |
| 18 | 2023 | 9 | |
| 19 | 2021 | 9 | |
| 20 | 2024 | 9 |
About Muyi Sun
Muyi Sun is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Ophthalmology and Cardiology and Cardiovascular Medicine, having authored 40 papers that have together received 575 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (8 papers), Generative Adversarial Networks and Image Synthesis (7 papers), Retinal Imaging and Analysis (5 papers), AI in cancer detection (5 papers), Face recognition and analysis (5 papers), Human Pose and Action Recognition (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Digital Imaging for Blood Diseases (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (230 citations), Ophthalmology (69 citations), Radiology, Nuclear Medicine and Imaging (179 citations), Cardiology and Cardiovascular Medicine (113 citations) and Media Technology (38 citations). Muyi Sun has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Xiaoguang Zhou, Xingqun Qi, Hao Dang, Qing Chang, Zhenan Sun, Yiwen Luo, Qi Li, Pengkun Liu, Caifeng Shan and Wanting Zhou. Their work appears in journals such as IEEE Access, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Industrial Informatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics and International Journal of Computer Vision.
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.