Jun Ge
Impact in
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- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Transplantation top 5%
- Renal Transplantation Outcomes and Treatments
Papers in
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- Radiomics and Machine Learning in Medical Imaging 12
- Medical Imaging Techniques and Applications 9
- Cardiac Imaging and Diagnostics 3
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- AI in cancer detection 19
- Co-authors
- Berkman Sahiner (26 shared papers)Heang‐Ping Chan (26 shared papers)Lubomir M. Hadjiiski (26 shared papers)Chuan Zhou (24 shared papers)Jun Wei (23 shared papers)Mark A. Helvie (13 shared papers)Yiheng Zhang (8 shared papers)Mitchell M. Goodsitt (2 shared papers)
- Journals
- Medical Physics (10 papers)Radiology Artificial Intelligence (1 paper)Transplant International (1 paper)Academic Radiology (1 paper)Journal of Cardiovascular Translational Research (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Jun Ge
32 papers receiving 796 citations
Peers
Comparison fields: 5 of 67
- Radiology, Nuclear Medicine and Imaging 552
- Transplantation 57
- Artificial Intelligence 431
- Pulmonary and Respiratory Medicine 379
- Computer Vision and Pattern Recognition 181
Countries citing papers authored by Jun Ge
This map shows the geographic impact of Jun Ge'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 Jun Ge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Ge more than expected).
Fields of papers citing papers by Jun Ge
This network shows the impact of papers produced by Jun Ge. 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 Jun Ge. The network helps show where Jun Ge may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Ge, 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 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 211 | |
| 2 | 2005 | 79 | |
| 3 | 2007 | 78 | |
| 4 | 2006 | 65 | |
| 5 | 2007 | 64 | |
| 6 | 2007 | 44 | |
| 7 | 2018 | 37 | |
| 8 | 2006 | 29 | |
| 9 | 2006 | 29 | |
| 10 | 2007 | 22 | |
| 11 | 2007 | 21 | |
| 12 | 2007 | 18 | |
| 13 | 2019 | 16 | |
| 14 | 2021 | 14 | |
| 15 | 2008 | 12 | |
| 16 | 2021 | 12 | |
| 17 | 2007 | 10 | |
| 18 | 2006 | 10 | |
| 19 | 2008 | 9 | |
| 20 | 2007 | 7 |
About Jun Ge
Jun Ge is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Oncology and Surgery, having authored 33 papers that have together received 829 indexed citations. Recurring topics across this work include AI in cancer detection (19 papers), Digital Radiography and Breast Imaging (12 papers), Radiomics and Machine Learning in Medical Imaging (12 papers), Medical Imaging Techniques and Applications (9 papers), Colorectal Cancer Screening and Detection (8 papers), Advanced X-ray and CT Imaging (3 papers), Renal Transplantation Outcomes and Treatments (3 papers) and Cardiac Imaging and Diagnostics (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (552 citations), Transplantation (57 citations), Artificial Intelligence (431 citations), Pulmonary and Respiratory Medicine (379 citations) and Computer Vision and Pattern Recognition (181 citations). Jun Ge has collaborated with scholars based in United States and China. Frequent co-authors include Berkman Sahiner, Heang‐Ping Chan, Lubomir M. Hadjiiski, Chuan Zhou, Jun Wei, Mark A. Helvie, Yiheng Zhang, Mitchell M. Goodsitt, Yiheng Zhang and Marilyn A. Roubidoux. Their work appears in journals such as Medical Physics, Radiology Artificial Intelligence, Transplant International, Academic Radiology and Journal of Cardiovascular Translational Research.
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.