Shuli Guo
- Biomedical Engineering
- Radiology, Nuclear Medicine and Imaging
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Cardiology and Cardiovascular Medicine
- Topics
- Radiomics and Machine Learning in Medical Imaging (4 papers)COVID-19 diagnosis using AI (3 papers)Stability and Control of Uncertain Systems (3 papers)
- Cited by
- Modeling and SimulationComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Journals
- Journal of the American College of CardiologyPhysics in Medicine and BiologyEcotoxicology and Environmental Safety
- Partner nations
- ChinaUnited KingdomFiji
In The Last Decade
Shuli Guo
27 papers receiving 237 citations
Peers
Comparison fields: 5 of 101
- Biomedical Engineering 50
- Radiology, Nuclear Medicine and Imaging 38
- Computer Vision and Pattern Recognition 35
- Artificial Intelligence 35
- Cardiology and Cardiovascular Medicine 33
Countries citing papers authored by Shuli Guo
This map shows the geographic impact of Shuli Guo'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 Shuli Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuli Guo more than expected).
Fields of papers citing papers by Shuli Guo
This network shows the impact of papers produced by Shuli Guo. 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 Shuli Guo. The network helps show where Shuli Guo may publish in the future.
Co-authorship network of co-authors of Shuli Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Shuli Guo. A scholar is included among the top collaborators of Shuli Guo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shuli Guo. Shuli Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 3 | |
| 7 | 16 | |
| 8 | 3 | |
| 9 | 60 | |
| 10 | 3 | |
| 11 | 36 | |
| 12 | 19 | |
| 13 | 2 | |
| 14 | 9 | |
| 15 | 14 | |
| 16 | 7 | |
| 17 | 1 | |
| 18 | 6 | |
| 19 | 3 | |
| 20 | 0 |
About Shuli Guo
Shuli Guo is a scholar working on Media Technology, Modeling and Simulation and Statistics, Probability and Uncertainty, having authored 32 papers that have together received 242 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), COVID-19 diagnosis using AI (3 papers) and Stability and Control of Uncertain Systems (3 papers). The work is most often cited by research in Modeling and Simulation (21 citations), Computer Vision and Pattern Recognition (35 citations) and Radiology, Nuclear Medicine and Imaging (38 citations). Shuli Guo has collaborated with scholars based in China, United Kingdom and Fiji. Frequent co-authors include Lina Han, Guowei Wang, Rohitash Chandra, Wentao Yang, Liming Cai, Shuping Wang, Yanhui Cao, Yuan Huang, Kaifeng Chen and Xiaoqiang Guo. Their work appears in journals such as Journal of the American College of Cardiology, Physics in Medicine and Biology and Ecotoxicology and Environmental Safety.
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.