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
- Imbalanced Data Classification Techniques 1
- Bayesian Modeling and Causal Inference 1
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 1
- Retinal Imaging and Analysis 1
- Co-authors
- Yizhou Wang (5 shared papers)Jianwei Wang (2 shared papers)Zhen Zhou (2 shared papers)Xiatian Zhu (1 shared paper)Wei‐Shi Zheng (1 shared paper)Xinwei Sun (4 shared papers)Jing Li (1 shared paper)Yao Huang (1 shared paper)
- Journals
- European Radiology (1 paper)IEEE Transactions on Image Processing (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Botong Wu
8 papers receiving 256 citations
Peers
Comparison fields: 5 of 50
- Radiology, Nuclear Medicine and Imaging 98
- Pulmonary and Respiratory Medicine 109
- Computer Vision and Pattern Recognition 65
- Artificial Intelligence 67
- Health Information Management 5
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 22 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 | 81 | |
| 2 | 2019 | 71 | |
| 3 | 2017 | 56 | |
| 4 | 2021 | 24 | |
| 5 | 2019 | 18 | |
| 6 | Recovering Latent Causal Factor for Generalization to Distributional Shifts | 2021 | 13 |
| 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 267 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (2 papers), COVID-19 diagnosis using AI (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Imbalanced Data Classification Techniques (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Retinal Imaging and Analysis (1 paper), Retinal Diseases and Treatments (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (98 citations), Pulmonary and Respiratory Medicine (109 citations), Computer Vision and Pattern Recognition (65 citations), Artificial Intelligence (67 citations) and Health Information Management (5 citations). Botong Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yizhou Wang, Jianwei Wang, Zhen Zhou, Xiatian Zhu, Wei‐Shi Zheng, Xinwei Sun, Jing Li, Yao Huang, Lina Zhou and Ning Wu. Their work appears in journals such as European Radiology, IEEE Transactions on Image Processing 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.