Botong Wu
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
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- Lung Cancer Diagnosis and Treatment
Papers in
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- Machine Learning in Healthcare 2
- Bayesian Modeling and Causal Inference 1
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- COVID-19 diagnosis using AI 2
- Co-authors
- Zhen Zhou (2 shared papers)Yizhou Wang (2 shared papers)Jianwei Wang (2 shared papers)Wei‐Shi Zheng (1 shared paper)Xiatian Zhu (1 shared paper)Xinwei Sun (4 shared papers)Yizhou Wang (3 shared papers)Jing Li (1 shared paper)
- Journals
- IEEE Transactions on Image Processing (1 paper)European Radiology (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Botong Wu
8 papers receiving 243 citations
Peers
Comparison fields: 5 of 51
- Radiology, Nuclear Medicine and Imaging 137
- Pulmonary and Respiratory Medicine 121
- Computer Vision and Pattern Recognition 63
- Artificial Intelligence 74
- Microbiology 1
Countries citing papers authored by Botong Wu
This map shows the geographic impact of Botong 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 Botong Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Botong Wu more than expected).
Fields of papers citing papers by Botong Wu
This network shows the impact of papers produced by Botong 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 Botong Wu. The network helps show where Botong Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Botong 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 | 2018 | 79 | |
| 2 | 2019 | 67 | |
| 3 | 2017 | 54 | |
| 4 | 2021 | 21 | |
| 5 | 2019 | 16 | |
| 6 | Recovering Latent Causal Factor for Generalization to Distributional Shifts | 2021 | 12 |
| 7 | 2012 | 3 | |
| 8 | 2021 | 1 |
About Botong Wu
Botong Wu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition and Ophthalmology, having authored 8 papers that have together received 253 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (2 papers), COVID-19 diagnosis using AI (2 papers), Machine Learning in Healthcare (2 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (1 paper), Glaucoma and retinal disorders (1 paper), Bayesian Modeling and Causal Inference (1 paper), Medical Imaging and Pathology Studies (1 paper) and Video Surveillance and Tracking Methods (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (137 citations), Pulmonary and Respiratory Medicine (121 citations), Computer Vision and Pattern Recognition (63 citations), Artificial Intelligence (74 citations) and Microbiology (1 citation). Botong Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Zhen Zhou, Yizhou Wang, Jianwei Wang, Wei‐Shi Zheng, Xiatian Zhu, Xinwei Sun, Yizhou Wang, Jing Li, Meng Li and Ning Wu. Their work appears in journals such as IEEE Transactions on Image Processing, European Radiology and Neural Information Processing Systems.
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.