Jinshan Tang
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 1%
- Media Technology top 0.5%
- Biomedical Engineering top 10%
- Co-authors
- Xiaoming LiuScott T. ActonQingling SunEli PeliRangaraj M. RangayyanJun XuYongyi YangIssam El Naqa
- Topics
- Image and Signal Denoising Methods (24 papers)Image Retrieval and Classification Techniques (21 papers)Medical Image Segmentation Techniques (16 papers)
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyRadiology, Nuclear Medicine and Imaging
- Partner nations
- United StatesChinaCanada
In The Last Decade
Jinshan Tang
145 papers receiving 3.4k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Computer Vision and Pattern Recognition 1.7k
- Artificial Intelligence 1.2k
- Radiology, Nuclear Medicine and Imaging 1.1k
- Media Technology 553
- Biomedical Engineering 405
Countries citing papers authored by Jinshan Tang
This map shows the geographic impact of Jinshan Tang'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 Jinshan Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinshan Tang more than expected).
Fields of papers citing papers by Jinshan Tang
This network shows the impact of papers produced by Jinshan Tang. 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 Jinshan Tang. The network helps show where Jinshan Tang may publish in the future.
Co-authorship network of co-authors of Jinshan Tang
This figure shows the co-authorship network connecting the top 25 collaborators of Jinshan Tang. A scholar is included among the top collaborators of Jinshan Tang 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 Jinshan Tang. Jinshan Tang 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 | 1 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 2 | |
| 9 | 3 | |
| 10 | 47 | |
| 11 | 5 | |
| 12 | 60 | |
| 13 | 18 | |
| 14 | 77 | |
| 15 | 5 | |
| 16 | Landcover Classification Using Deep Fully Convolutional Neural Networks | 1 |
| 17 | A texture features based medical image retrieval system for breast cancer | 1 |
| 18 | 14 | |
| 19 | 64 | |
| 20 | 64 |
About Jinshan Tang
Jinshan Tang is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Radiology, Nuclear Medicine and Imaging, having authored 154 papers that have together received 3.6k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (24 papers), Image Retrieval and Classification Techniques (21 papers) and Medical Image Segmentation Techniques (16 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.7k citations), Media Technology (553 citations) and Radiology, Nuclear Medicine and Imaging (1.1k citations). Jinshan Tang has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Xiaoming Liu, Scott T. Acton, Qingling Sun, Eli Peli, Rangaraj M. Rangayyan, Jun Xu, Yongyi Yang, Issam El Naqa, Zilong Hu and Kai Zhang. Their work appears in journals such as Journal of Cleaner Production, Environmental Pollution and IEEE Access.
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.