Junxi Chen
Impact in
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- Digital Media Forensic Detection
- Advanced Steganography and Watermarking Techniques
- Chaos-based Image/Signal Encryption
- Advanced Neural Network Applications
- Analytical Chemistry top 10%
Papers in
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- Advanced Neural Network Applications 6
- Human Pose and Action Recognition 2
- Advanced Steganography and Watermarking Techniques 2
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- AI in cancer detection 6
- Anomaly Detection Techniques and Applications 3
- Co-authors
- Shaohua Tang (2 shared papers)Junhui He (2 shared papers)Xiaoling Deng (2 shared papers)Yubin Lan (1 shared paper)Xu Wang (1 shared paper)Sha Chen (1 shared paper)Yixiang Duan (1 shared paper)Mingzhi Chen (4 shared papers)
In The Last Decade
Junxi Chen
32 papers receiving 388 citations
Peers
Comparison fields: 5 of 77
- Computer Vision and Pattern Recognition 126
- Analytical Chemistry 58
- Artificial Intelligence 106
- Horticulture 3
- Radiology, Nuclear Medicine and Imaging 60
Countries citing papers authored by Junxi Chen
This map shows the geographic impact of Junxi Chen'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 Junxi Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junxi Chen more than expected).
Fields of papers citing papers by Junxi Chen
This network shows the impact of papers produced by Junxi Chen. 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 Junxi Chen. The network helps show where Junxi Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Junxi Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 71 | |
| 2 | 2016 | 57 | |
| 3 | 2019 | 42 | |
| 4 | 2023 | 22 | |
| 5 | 2022 | 19 | |
| 6 | 2023 | 18 | |
| 7 | 2024 | 16 | |
| 8 | 2023 | 15 | |
| 9 | 2021 | 13 | |
| 10 | 2023 | 11 | |
| 11 | 2022 | 11 | |
| 12 | 2022 | 11 | |
| 13 | 2016 | 11 | |
| 14 | 2023 | 10 | |
| 15 | 2016 | 9 | |
| 16 | 2018 | 8 | |
| 17 | 2024 | 6 | |
| 18 | 2021 | 6 | |
| 19 | 2017 | 6 | |
| 20 | 2023 | 5 |
About Junxi Chen
Junxi Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications and Hardware and Architecture, having authored 36 papers that have together received 401 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (6 papers), AI in cancer detection (6 papers), COVID-19 diagnosis using AI (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Anomaly Detection Techniques and Applications (3 papers), Brain Tumor Detection and Classification (3 papers), Human Pose and Action Recognition (2 papers) and Advanced Steganography and Watermarking Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (126 citations), Analytical Chemistry (58 citations), Artificial Intelligence (106 citations), Horticulture (3 citations) and Radiology, Nuclear Medicine and Imaging (60 citations). Junxi Chen has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Shaohua Tang, Junhui He, Xiaoling Deng, Yubin Lan, Xu Wang, Sha Chen, Yixiang Duan, Mingzhi Chen, Jorge Pisonero and Lingna Chen. Their work appears in journals such as Computers in Biology and Medicine, Journal of Applied Biomedicine, Medical Physics, World Neurosurgery and Materials Science and Engineering A.
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.