Pu Huang
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- Digital Imaging for Blood Diseases 4
- Generative Adversarial Networks and Image Synthesis 3
- Medical Image Segmentation Techniques 2
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- Medical Imaging Techniques and Applications 3
- Retinal Imaging and Analysis 3
- COVID-19 diagnosis using AI 3
- Radiation top 10%
- Advanced Radiotherapy Techniques 5
- Artificial Intelligence top 10%
- AI in cancer detection 6
- Co-authors
- Dengwang LiCong LiuMeirong ChenMin ChenXiangfei ChaiZekun JiangJie LiHua Zhang
- Journals
- Medical Physics (2 papers)Journal of Applied Clinical Medical Physics (2 papers)Medical Image Analysis (2 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Pu Huang
27 papers receiving 437 citations
Peers
Comparison fields: 5 of 98
- Computer Vision and Pattern Recognition 192
- Radiology, Nuclear Medicine and Imaging 163
- Radiation 52
- Neurology 43
- Artificial Intelligence 133
Countries citing papers authored by Pu Huang
This map shows the geographic impact of Pu Huang'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 Pu Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pu Huang more than expected).
Fields of papers citing papers by Pu Huang
This network shows the impact of papers produced by Pu Huang. 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 Pu Huang. The network helps show where Pu Huang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pu Huang, 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 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 5 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 4 | |
| 7 | 2022 | 23 | |
| 8 | 2021 | 7 | |
| 9 | 2021 | 106 | |
| 10 | 2021 | 23 | |
| 11 | 2020 | 9 | |
| 12 | 2020 | 23 | |
| 13 | 2020 | 24 | |
| 14 | 2019 | 21 | |
| 15 | 2019 | 23 | |
| 16 | 2019 | 18 | |
| 17 | 2019 | 13 | |
| 18 | 2017 | 5 | |
| 19 | 2016 | 5 | |
| 20 | 2008 | 2 |
About Pu Huang
Pu Huang is a scholar working on Computer Vision and Pattern Recognition, Radiation, Radiology, Nuclear Medicine and Imaging, Biophysics and Software, having authored 29 papers that have together received 445 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Advanced Radiotherapy Techniques (5 papers), Digital Imaging for Blood Diseases (4 papers), Medical Imaging Techniques and Applications (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Retinal Imaging and Analysis (3 papers), COVID-19 diagnosis using AI (3 papers) and Medical Image Segmentation Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (192 citations), Radiology, Nuclear Medicine and Imaging (163 citations), Radiation (52 citations), Neurology (43 citations) and Artificial Intelligence (133 citations). Pu Huang has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Dengwang Li, Cong Liu, Meirong Chen, Min Chen, Xiangfei Chai, Zekun Jiang, Jie Li, Hua Zhang, Yan Zhang and Jianbo Wang. Their work appears in journals such as Medical Physics, Journal of Applied Clinical Medical Physics, Medical Image Analysis, Advanced Engineering Informatics and Engineering Applications of Artificial Intelligence.
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.