Yunguan Fu
- Computer Vision and Pattern Recognition
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Computational Mechanics
- Biomedical Engineering
- Co-authors
- Yipeng HuMingyan LiuQianye YangDean C. BarrattMatthew J. ClarksonZachary M. C. BaumJ. Alison NobleZhe Min
- Topics
- Radiomics and Machine Learning in Medical Imaging (5 papers)Medical Image Segmentation Techniques (4 papers)Medical Imaging and Analysis (3 papers)
- Cited by
- Health InformaticsComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Journals
- International Journal of Radiation Oncology*Biology*PhysicsIEEE Transactions on Medical ImagingPhysics in Medicine and Biology
- Partner nations
- United KingdomUnited StatesHong Kong
In The Last Decade
Yunguan Fu
12 papers receiving 121 citations
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 43
- Radiology, Nuclear Medicine and Imaging 41
- Artificial Intelligence 25
- Computational Mechanics 24
- Biomedical Engineering 23
Countries citing papers authored by Yunguan Fu
This map shows the geographic impact of Yunguan Fu'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 Yunguan Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunguan Fu more than expected).
Fields of papers citing papers by Yunguan Fu
This network shows the impact of papers produced by Yunguan Fu. 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 Yunguan Fu. The network helps show where Yunguan Fu may publish in the future.
Co-authorship network of co-authors of Yunguan Fu
This figure shows the co-authorship network connecting the top 25 collaborators of Yunguan Fu. A scholar is included among the top collaborators of Yunguan Fu 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 Yunguan Fu. Yunguan Fu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 14 | |
| 4 | 19 | |
| 5 | 5 | |
| 6 | 28 | |
| 7 | 5 | |
| 8 | 4 | |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 18 | |
| 13 | 20 |
About Yunguan Fu
Yunguan Fu is a scholar working on Computer Vision and Pattern Recognition, Radiation and Radiology, Nuclear Medicine and Imaging, having authored 13 papers that have together received 122 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), Medical Image Segmentation Techniques (4 papers) and Medical Imaging and Analysis (3 papers). The work is most often cited by research in Health Informatics (4 citations), Computer Vision and Pattern Recognition (43 citations) and Radiology, Nuclear Medicine and Imaging (41 citations). Yunguan Fu has collaborated with scholars based in United Kingdom, United States and Hong Kong. Frequent co-authors include Yipeng Hu, Mingyan Liu, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Zachary M. C. Baum, J. Alison Noble, Zhe Min, Vasilis Stavrinides and Richard E. Fan. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging and Physics in Medicine and Biology.
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.