Shujun Wang
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Ophthalmology top 5%
- Biomedical Engineering
- Topics
- Medical Image Segmentation Techniques (4 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)AI in cancer detection (4 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingOphthalmologyComputer Vision and Pattern Recognition
- Journals
- Nature CommunicationsSHILAP Revista de lepidopterologíaFrontiers in Immunology
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Shujun Wang
25 papers receiving 682 citations
Peers
Comparison fields: 5 of 89
- Radiology, Nuclear Medicine and Imaging 407
- Artificial Intelligence 306
- Computer Vision and Pattern Recognition 263
- Ophthalmology 133
- Biomedical Engineering 103
Countries citing papers authored by Shujun Wang
This map shows the geographic impact of Shujun Wang'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 Shujun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shujun Wang more than expected).
Fields of papers citing papers by Shujun Wang
This network shows the impact of papers produced by Shujun Wang. 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 Shujun Wang. The network helps show where Shujun Wang may publish in the future.
Co-authorship network of co-authors of Shujun Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Shujun Wang. A scholar is included among the top collaborators of Shujun Wang 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 Shujun Wang. Shujun Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 9 | |
| 9 | 1 | |
| 10 | 0 | |
| 11 | 3 | |
| 12 | 5 | |
| 13 | 107 | |
| 14 | 199 | |
| 15 | 137 | |
| 16 | 7 | |
| 17 | 21 | |
| 18 | 9 | |
| 19 | 29 | |
| 20 | Application of RS encoding and decoding method in tactical data link | 1 |
About Shujun Wang
Shujun Wang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 30 papers that have together received 694 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and AI in cancer detection (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (407 citations), Ophthalmology (133 citations) and Computer Vision and Pattern Recognition (263 citations). Shujun Wang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Lequan Yu, Pheng‐Ann Heng, Chi‐Wing Fu, Xin Yang, Kang Li, Yaxi Zhu, Hao Chen, Xinjuan Fan, Xiang‐Bo Wan and Huangjing Lin. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Frontiers in Immunology.
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.