Jun Shi
Impact in
- Health Informatics top 2%
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Ultrasound Imaging and Elastography
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 32
- Ultrasound Imaging and Elastography 14
-
- AI in cancer detection 44
- Co-authors
- Shihui Ying (37 shared papers)Qi Zhang (21 shared papers)Yong‐Ping Zheng (20 shared papers)Zheng Xiao (7 shared papers)Jun Wang (35 shared papers)Qi Zhang (3 shared papers)Yan Li (3 shared papers)Shichong Zhou (11 shared papers)
- Journals
- IEEE Journal of Biomedical and Health Informatics (12 papers)IEEE Transactions on Medical Imaging (8 papers)Neurocomputing (6 papers)Computers in Biology and Medicine (6 papers)Pattern Recognition (5 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Jun Shi
178 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 163
- Health Informatics 82
- Radiology, Nuclear Medicine and Imaging 1.2k
- Neurology 393
- Artificial Intelligence 1.4k
- Computer Vision and Pattern Recognition 779
Countries citing papers authored by Jun Shi
This map shows the geographic impact of Jun Shi'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 Jun Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Shi more than expected).
Fields of papers citing papers by Jun Shi
This network shows the impact of papers produced by Jun Shi. 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 Jun Shi. The network helps show where Jun Shi may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Shi, 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 188 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 324 | |
| 2 | 2016 | 180 | |
| 3 | 2016 | 139 | |
| 4 | 2012 | 115 | |
| 5 | 2008 | 104 | |
| 6 | 2013 | 100 | |
| 7 | 2020 | 93 | |
| 8 | 2018 | 90 | |
| 9 | 2017 | 89 | |
| 10 | 2020 | 89 | |
| 11 | 2018 | 80 | |
| 12 | 2017 | 68 | |
| 13 | 2018 | 62 | |
| 14 | 2016 | 62 | |
| 15 | 2020 | 60 | |
| 16 | 2017 | 58 | |
| 17 | 2021 | 55 | |
| 18 | 2021 | 54 | |
| 19 | 2010 | 51 | |
| 20 | 2012 | 50 |
About Jun Shi
Jun Shi is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 188 papers that have together received 3.6k indexed citations. Recurring topics across this work include AI in cancer detection (44 papers), Radiomics and Machine Learning in Medical Imaging (32 papers), Muscle activation and electromyography studies (20 papers), Brain Tumor Detection and Classification (17 papers), Photoacoustic and Ultrasonic Imaging (16 papers), EEG and Brain-Computer Interfaces (15 papers), Ultrasound Imaging and Elastography (14 papers) and Image and Signal Denoising Methods (13 papers). The work is most often cited by research in Health Informatics (82 citations), Radiology, Nuclear Medicine and Imaging (1.2k citations), Neurology (393 citations), Artificial Intelligence (1.4k citations) and Computer Vision and Pattern Recognition (779 citations). Jun Shi has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Shihui Ying, Qi Zhang, Yong‐Ping Zheng, Zheng Xiao, Jun Wang, Qi Zhang, Yan Li, Shichong Zhou, Yang Xiao and Hairong Zheng. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Medical Imaging, Neurocomputing, Computers in Biology and Medicine and Pattern Recognition.
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.