Ran Gu
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in
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- Radiomics and Machine Learning in Medical Imaging 9
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- Advanced Neural Network Applications 7
- Medical Image Segmentation Techniques 5
- Co-authors
- Guotai Wang (16 shared papers)Shaoting Zhang (10 shared papers)Wenhui Lei (10 shared papers)Jingyang Zhang (6 shared papers)Shichuan Zhang (5 shared papers)Kang Li (3 shared papers)Yaping Yang (11 shared papers)Feng-tao Liu (8 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (4 papers)Journal of Surgical Research (2 papers)European Radiology (2 papers)Neurocomputing (2 papers)BMC Cancer (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Ran Gu
38 papers receiving 508 citations
Peers
Comparison fields: 5 of 67
- Radiology, Nuclear Medicine and Imaging 180
- Computer Vision and Pattern Recognition 179
- Neurology 42
- Artificial Intelligence 159
- Otorhinolaryngology 13
Countries citing papers authored by Ran Gu
This map shows the geographic impact of Ran Gu'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 Ran Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ran Gu more than expected).
Fields of papers citing papers by Ran Gu
This network shows the impact of papers produced by Ran Gu. 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 Ran Gu. The network helps show where Ran Gu may publish in the future.
Co-authors
The 25 scholars most cited alongside Ran Gu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 49 | |
| 2 | 2023 | 43 | |
| 3 | 2023 | 40 | |
| 4 | 2023 | 34 | |
| 5 | 2012 | 28 | |
| 6 | 2021 | 27 | |
| 7 | 2021 | 25 | |
| 8 | 2021 | 24 | |
| 9 | 2021 | 22 | |
| 10 | 2015 | 21 | |
| 11 | 2023 | 21 | |
| 12 | 2022 | 20 | |
| 13 | 2018 | 16 | |
| 14 | 2021 | 16 | |
| 15 | 2021 | 14 | |
| 16 | 2020 | 12 | |
| 17 | 2019 | 11 | |
| 18 | 2021 | 10 | |
| 19 | 2019 | 8 | |
| 20 | 2019 | 8 |
About Ran Gu
Ran Gu is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Pathology and Forensic Medicine, Artificial Intelligence and Otorhinolaryngology, having authored 40 papers that have together received 510 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Domain Adaptation and Few-Shot Learning (7 papers), Advanced Neural Network Applications (7 papers), Breast Lesions and Carcinomas (6 papers), Medical Image Segmentation Techniques (5 papers), Medical Imaging and Analysis (4 papers), Head and Neck Cancer Studies (3 papers) and Breast Cancer Treatment Studies (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (180 citations), Computer Vision and Pattern Recognition (179 citations), Neurology (42 citations), Artificial Intelligence (159 citations) and Otorhinolaryngology (13 citations). Ran Gu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Guotai Wang, Shaoting Zhang, Wenhui Lei, Jingyang Zhang, Shichuan Zhang, Kang Li, Yaping Yang, Feng-tao Liu, Qiang Liu and Lixu Gu. Their work appears in journals such as IEEE Transactions on Medical Imaging, Journal of Surgical Research, European Radiology, Neurocomputing and BMC Cancer.
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.