Xianjun Fu
Impact in
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Digital Imaging for Blood Diseases
- Advanced Image and Video Retrieval Techniques
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- Industrial Vision Systems and Defect Detection
Papers in
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- Digital Imaging for Blood Diseases 2
- Image Enhancement Techniques 1
- Image and Object Detection Techniques 1
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- Machine Learning in Healthcare 1
- Co-authors
- Ben Chen (1 shared paper)Yiyu Huang (1 shared paper)Feiwei Qin (1 shared paper)Yuxing Dai (1 shared paper)Yu Gao (1 shared paper)Chenyan Zhang (1 shared paper)Changmiao Wang (2 shared papers)Yifei Chen (1 shared paper)
In The Last Decade
Xianjun Fu
6 papers receiving 234 citations
Xianjun Fu's Hit Papers
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 106
- Industrial and Manufacturing Engineering 44
- Health Informatics 5
- Media Technology 26
- Oral Surgery 9
Countries citing papers authored by Xianjun Fu
This map shows the geographic impact of Xianjun Fu'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 Xianjun Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xianjun Fu more than expected).
Fields of papers citing papers by Xianjun Fu
This network shows the impact of papers produced by Xianjun Fu. 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 Xianjun Fu. The network helps show where Xianjun Fu may publish in the future.
Co-authors
The 25 scholars most cited alongside Xianjun Fu, 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 | Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases Hit paper breakdown → | 2024 | 191 |
| 2 | 2018 | 21 | |
| 3 | 2021 | 17 | |
| 4 | 2023 | 6 | |
| 5 | 2024 | 2 | |
| 6 | 2025 | 1 | |
| 7 | 2025 | 0 |
About Xianjun Fu
Xianjun Fu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Neurology and Epidemiology, having authored 7 papers that have together received 238 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (2 papers), Forensic Anthropology and Bioarchaeology Studies (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Electrospun Nanofibers in Biomedical Applications (1 paper), Image Enhancement Techniques (1 paper), Hemostasis and retained surgical items (1 paper), Machine Learning in Healthcare (1 paper) and Image and Object Detection Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (106 citations), Industrial and Manufacturing Engineering (44 citations), Health Informatics (5 citations), Media Technology (26 citations) and Oral Surgery (9 citations). Xianjun Fu has collaborated with scholars based in China and Australia. Frequent co-authors include Ben Chen, Yiyu Huang, Feiwei Qin, Yuxing Dai, Yu Gao, Chenyan Zhang, Changmiao Wang, Yifei Chen, Yong Peng and Qingmao Hu. Their work appears in journals such as Computer Methods and Programs in Biomedicine, IEEE Access, Computers in Biology and Medicine, Colloids and Surfaces B Biointerfaces and Applied Sciences.
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.