Dinggang Shen
- Computer Vision and Pattern Recognition top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Neurology top 5%
- Co-authors
- Christos DavatzikosHeung‐Il SukSeong‐Whan LeeTianming LiuYuanjie ZhengKun SunYue WangFuhua Yan
- Topics
- Medical Image Segmentation Techniques (10 papers)Brain Tumor Detection and Classification (6 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Dinggang Shen
27 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Computer Vision and Pattern Recognition 576
- Radiology, Nuclear Medicine and Imaging 490
- Cognitive Neuroscience 207
- Artificial Intelligence 189
- Neurology 166
Countries citing papers authored by Dinggang Shen
This map shows the geographic impact of Dinggang Shen'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 Dinggang Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dinggang Shen more than expected).
Fields of papers citing papers by Dinggang Shen
This network shows the impact of papers produced by Dinggang Shen. 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 Dinggang Shen. The network helps show where Dinggang Shen may publish in the future.
Co-authorship network of co-authors of Dinggang Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Dinggang Shen. A scholar is included among the top collaborators of Dinggang Shen 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 Dinggang Shen. Dinggang Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 20 | |
| 5 | 16 | |
| 6 | 14 | |
| 7 | 32 | |
| 8 | 14 | |
| 9 | 47 | |
| 10 | 8 | |
| 11 | 31 | |
| 12 | 37 | |
| 13 | 48 | |
| 14 | 14 | |
| 15 | 2 | |
| 16 | 10 | |
| 17 | 4 | |
| 18 | 2 | |
| 19 | HAMMER: hierarchical attribute matching mechanism for elastic registrationbreakdown → | 847 |
| 20 | 18 |
About Dinggang Shen
Dinggang Shen is a scholar working on Health Informatics, Neurology and Radiology, Nuclear Medicine and Imaging, having authored 27 papers that have together received 1.2k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (10 papers), Brain Tumor Detection and Classification (6 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (576 citations), Radiology, Nuclear Medicine and Imaging (490 citations) and Neurology (166 citations). Dinggang Shen has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Christos Davatzikos, Heung‐Il Suk, Seong‐Whan Lee, Tianming Liu, Yuanjie Zheng, Kun Sun, Yue Wang, Fuhua Yan, Li Wang and Shuai Wang. Their work appears in journals such as Radiology, IEEE Access and IEEE Transactions on Medical Imaging.
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.