Fa Wu
Impact in
- Health Informatics top 2%
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Medical Image Segmentation Techniques 3
- Advanced Neural Network Applications 2
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- Reinforcement Learning in Robotics 2
- AI in cancer detection 2
- Co-authors
- De-Xing Kong (9 shared papers)Peijun Hu (3 shared papers)Jinlian Ma (5 shared papers)Jiang Zhu (2 shared papers)Jialin Peng (2 shared papers)Tianan Jiang (3 shared papers)Dexing Kong (6 shared papers)Lu Fang (2 shared papers)
In The Last Decade
Fa Wu
18 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 83
- Health Informatics 78
- Radiology, Nuclear Medicine and Imaging 558
- Computer Vision and Pattern Recognition 318
- Artificial Intelligence 422
- Endocrinology, Diabetes and Metabolism 170
Countries citing papers authored by Fa Wu
This map shows the geographic impact of Fa Wu'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 Fa Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fa Wu more than expected).
Fields of papers citing papers by Fa Wu
This network shows the impact of papers produced by Fa Wu. 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 Fa Wu. The network helps show where Fa Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Fa Wu, 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 | 2016 | 218 | |
| 2 | 2016 | 207 | |
| 3 | 2016 | 162 | |
| 4 | 2016 | 155 | |
| 5 | 2017 | 144 | |
| 6 | 2017 | 103 | |
| 7 | 2018 | 26 | |
| 8 | 2021 | 21 | |
| 9 | 2020 | 15 | |
| 10 | 2023 | 8 | |
| 11 | 2022 | 6 | |
| 12 | 2024 | 3 | |
| 13 | 2026 | 1 | |
| 14 | 2013 | 1 | |
| 15 | 2016 | 1 | |
| 16 | 2024 | 1 | |
| 17 | 2022 | 1 | |
| 18 | 2020 | 1 | |
| 19 | 2018 | 1 | |
| 20 | 2017 | 0 |
About Fa Wu
Fa Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Endocrinology, Diabetes and Metabolism, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering, having authored 20 papers that have together received 1.1k indexed citations. Recurring topics across this work include Thyroid Cancer Diagnosis and Treatment (4 papers), Medical Image Segmentation Techniques (3 papers), Advanced Neural Network Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Reinforcement Learning in Robotics (2 papers), Medical Imaging and Analysis (2 papers), AI in cancer detection (2 papers) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Health Informatics (78 citations), Radiology, Nuclear Medicine and Imaging (558 citations), Computer Vision and Pattern Recognition (318 citations), Artificial Intelligence (422 citations) and Endocrinology, Diabetes and Metabolism (170 citations). Fa Wu has collaborated with scholars based in China, Vietnam and Hong Kong. Frequent co-authors include De-Xing Kong, Peijun Hu, Jinlian Ma, Jiang Zhu, Jialin Peng, Tianan Jiang, Dexing Kong, Lu Fang, Zhiyi Peng and Dong Xu. Their work appears in journals such as International Journal of Computer Assisted Radiology and Surgery, Medical Physics, Physics in Medicine and Biology, European Journal of Radiology and Ultrasonics.
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.