Xiaoben Jiang
Impact in
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- Image and Signal Denoising Methods
- Advanced Neural Network Applications
Papers in
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- Image and Signal Denoising Methods 6
- Advanced Image Processing Techniques 3
- Digital Imaging for Blood Diseases 2
- Face recognition and analysis 2
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- COVID-19 diagnosis using AI 4
- Radiomics and Machine Learning in Medical Imaging 2
- Co-authors
- Yu Zhu (8 shared papers)Dawei Yang (3 shared papers)Yan Jin (4 shared papers)Yue Wu (1 shared paper)Jiajun Lin (3 shared papers)Yu Yao (2 shared papers)Roy E. Weber (1 shared paper)Jay F. Storz (1 shared paper)
- Journals
- The Visual Computer (4 papers)Machine Vision and Applications (2 papers)Journal of Visual Communication and Image Representation (1 paper)Computer Methods and Programs in Biomedicine (1 paper)IEEE Transactions on Information Forensics and Security (1 paper)
- Partner nations
- ChinaUnited StatesDenmark
In The Last Decade
Xiaoben Jiang
14 papers receiving 328 citations
Peers
Comparison fields: 5 of 81
- Computer Vision and Pattern Recognition 132
- Health Informatics 6
- Radiology, Nuclear Medicine and Imaging 90
- Media Technology 30
- Oncology 59
Countries citing papers authored by Xiaoben Jiang
This map shows the geographic impact of Xiaoben Jiang'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 Xiaoben Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoben Jiang more than expected).
Fields of papers citing papers by Xiaoben Jiang
This network shows the impact of papers produced by Xiaoben Jiang. 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 Xiaoben Jiang. The network helps show where Xiaoben Jiang may publish in the future.
Co-authors
The 20 scholars most cited alongside Xiaoben Jiang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 98 | |
| 2 | 2011 | 46 | |
| 3 | 2020 | 31 | |
| 4 | 2022 | 30 | |
| 5 | 2023 | 22 | |
| 6 | 2019 | 22 | |
| 7 | 2022 | 21 | |
| 8 | 2022 | 17 | |
| 9 | 2021 | 11 | |
| 10 | 2022 | 11 | |
| 11 | 2024 | 9 | |
| 12 | 2023 | 9 | |
| 13 | 2019 | 8 | |
| 14 | 2020 | 1 |
About Xiaoben Jiang
Xiaoben Jiang is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Media Technology and Dermatology, having authored 14 papers that have together received 336 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (6 papers), COVID-19 diagnosis using AI (4 papers), Advanced Image Processing Techniques (3 papers), Advanced Image Fusion Techniques (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Digital Imaging for Blood Diseases (2 papers), Face recognition and analysis (2 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (132 citations), Health Informatics (6 citations), Radiology, Nuclear Medicine and Imaging (90 citations), Media Technology (30 citations) and Oncology (59 citations). Xiaoben Jiang has collaborated with scholars based in China, United States and Denmark. Frequent co-authors include Yu Zhu, Dawei Yang, Yan Jin, Yue Wu, Jiajun Lin, Yu Yao, Roy E. Weber, Jay F. Storz, Hideaki Moriyama and Chandrasekhar Natarajan. Their work appears in journals such as The Visual Computer, Machine Vision and Applications, Journal of Visual Communication and Image Representation, Computer Methods and Programs in Biomedicine and IEEE Transactions on Information Forensics and Security.
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.