Aaron Wu
Impact in
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Retinal Imaging and Analysis
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- Digital Imaging for Blood Diseases
- Medical Image Segmentation Techniques
Papers in
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- Lung Cancer Diagnosis and Treatment 2
- Digital Radiography and Breast Imaging 1
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- Radiation Dose and Imaging 1
- COVID-19 diagnosis using AI 1
- Radiomics and Machine Learning in Medical Imaging 1
- Retinal Imaging and Analysis 1
- Co-authors
- Daniel J. Mollura (4 shared papers)Ziyue Xu (4 shared papers)Mingchen Gao (4 shared papers)Le Lü (2 shared papers)Ronald M. Summers (2 shared papers)Ulaş Bağcı (2 shared papers)Georgios Z. Papadakis (2 shared papers)Adrien Depeursinge (1 shared paper)
- Journals
- Tomography (1 paper)Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (1 paper)
- Partner nations
- United StatesSwitzerland
In The Last Decade
Aaron Wu
4 papers receiving 255 citations
Peers
Comparison fields: 5 of 49
- Radiology, Nuclear Medicine and Imaging 192
- Computer Vision and Pattern Recognition 68
- Artificial Intelligence 101
- Neurology 26
- Ophthalmology 26
Countries citing papers authored by Aaron Wu
This map shows the geographic impact of Aaron 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 Aaron Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aaron Wu more than expected).
Fields of papers citing papers by Aaron Wu
This network shows the impact of papers produced by Aaron 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 Aaron Wu. The network helps show where Aaron Wu may publish in the future.
Co-authors
The 14 scholars most cited alongside Aaron 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 | 179 | |
| 2 | 2016 | 53 | |
| 3 | 2016 | 26 | |
| 4 | 2017 | 11 |
About Aaron Wu
Aaron Wu is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Ophthalmology and Ocean Engineering, having authored 4 papers that have together received 269 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (2 papers), Radiation Dose and Imaging (1 paper), Digital Imaging for Blood Diseases (1 paper), COVID-19 diagnosis using AI (1 paper), Glaucoma and retinal disorders (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Digital Radiography and Breast Imaging (1 paper) and Retinal Imaging and Analysis (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (192 citations), Computer Vision and Pattern Recognition (68 citations), Artificial Intelligence (101 citations), Neurology (26 citations) and Ophthalmology (26 citations). Aaron Wu has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Daniel J. Mollura, Ziyue Xu, Mingchen Gao, Le Lü, Ronald M. Summers, Ulaş Bağcı, Georgios Z. Papadakis, Adrien Depeursinge, Hoo-Chang Shin and Holger R. Roth. Their work appears in journals such as Tomography and Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization.
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.