Xiaoben Jiang
Impact in
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- Image and Signal Denoising Methods
- Advanced Neural Network Applications
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Image and Signal Denoising Methods 5
- Advanced Image Processing Techniques 3
- Digital Imaging for Blood Diseases 2
- Face recognition and analysis 2
- Advanced Neural Network Applications 2
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- Advanced Image Fusion Techniques 3
- Co-authors
- Yu Michael Zhu (8 shared papers)Dawei Yang (3 shared papers)Yan Jin (3 shared papers)Yue Wu (1 shared paper)Jiajun Lin (3 shared papers)Yatong Liu (3 shared papers)Yu Yao (1 shared paper)Roy E. Weber (1 shared paper)
- Journals
- The Visual Computer (3 papers)Machine Vision and Applications (2 papers)Neurocomputing (1 paper)Computer Methods and Programs in Biomedicine (1 paper)Biomedical Signal Processing and Control (1 paper)
- Partner nations
- ChinaDenmarkUnited States
In The Last Decade
Xiaoben Jiang
13 papers receiving 351 citations
Peers
Comparison fields: 5 of 81
- Computer Vision and Pattern Recognition 137
- Radiology, Nuclear Medicine and Imaging 93
- Health Informatics 6
- Media Technology 30
- Oncology 60
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 | 105 | |
| 2 | 2011 | 46 | |
| 3 | 2022 | 32 | |
| 4 | 2020 | 31 | |
| 5 | 2023 | 27 | |
| 6 | 2019 | 23 | |
| 7 | 2022 | 23 | |
| 8 | 2022 | 18 | |
| 9 | 2024 | 12 | |
| 10 | 2022 | 12 | |
| 11 | 2023 | 12 | |
| 12 | 2021 | 12 | |
| 13 | 2019 | 8 |
About Xiaoben Jiang
Xiaoben Jiang is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Dermatology, having authored 13 papers that have together received 361 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (5 papers), COVID-19 diagnosis using AI (4 papers), Advanced Image Processing Techniques (3 papers), Advanced Image Fusion Techniques (3 papers), Digital Imaging for Blood Diseases (2 papers), Face recognition and analysis (2 papers), AI in cancer detection (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (137 citations), Radiology, Nuclear Medicine and Imaging (93 citations), Health Informatics (6 citations), Media Technology (30 citations) and Oncology (60 citations). Xiaoben Jiang has collaborated with scholars based in China, Denmark and United States. Frequent co-authors include Yu Michael Zhu, Dawei Yang, Yan Jin, Yue Wu, Jiajun Lin, Yatong Liu, Yu Yao, Roy E. Weber, Jay F. Storz and Chandrasekhar Natarajan. Their work appears in journals such as The Visual Computer, Machine Vision and Applications, Neurocomputing, Computer Methods and Programs in Biomedicine and Biomedical Signal Processing and Control.
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.