Ming Fan
Impact in
-
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- COVID-19 diagnosis using AI
- Medical Imaging Techniques and Applications
- Health Informatics top 5%
Papers in
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- Radiomics and Machine Learning in Medical Imaging 33
- MRI in cancer diagnosis 19
- Medical Imaging Techniques and Applications 8
-
- AI in cancer detection 21
- Co-authors
- Lihua Li (56 shared papers)Xin Gao (23 shared papers)Juan Zhang (9 shared papers)Yu Li (3 shared papers)Ramzan Kh. Umarov (2 shared papers)Cheng Hu (5 shared papers)Peng hui Zhang (8 shared papers)Shiwei Wang (6 shared papers)
- Journals
- Physics in Medicine and Biology (7 papers)Bioinformatics (4 papers)IEEE Journal of Biomedical and Health Informatics (4 papers)Journal of Translational Medicine (3 papers)NeuroImage (2 papers)
- Partner nations
- ChinaSaudi ArabiaUnited States
In The Last Decade
Ming Fan
73 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 140
- Radiology, Nuclear Medicine and Imaging 819
- Health Informatics 50
- Artificial Intelligence 379
- Genetics 90
- Cancer Research 114
Countries citing papers authored by Ming Fan
This map shows the geographic impact of Ming Fan'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 Ming Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Fan more than expected).
Fields of papers citing papers by Ming Fan
This network shows the impact of papers produced by Ming Fan. 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 Ming Fan. The network helps show where Ming Fan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming Fan, 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 81 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 207 | |
| 2 | 2020 | 140 | |
| 3 | 2017 | 132 | |
| 4 | 2010 | 119 | |
| 5 | 2017 | 116 | |
| 6 | 2021 | 92 | |
| 7 | 2019 | 82 | |
| 8 | 2020 | 74 | |
| 9 | 2017 | 67 | |
| 10 | 2019 | 66 | |
| 11 | 2021 | 54 | |
| 12 | 2018 | 44 | |
| 13 | 2019 | 43 | |
| 14 | 2017 | 42 | |
| 15 | 2020 | 35 | |
| 16 | 2021 | 31 | |
| 17 | 2019 | 29 | |
| 18 | 2017 | 28 | |
| 19 | 2020 | 27 | |
| 20 | 2019 | 23 |
About Ming Fan
Ming Fan is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Cancer Research, Pulmonary and Respiratory Medicine and Molecular Biology, having authored 81 papers that have together received 1.8k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (33 papers), AI in cancer detection (21 papers), MRI in cancer diagnosis (19 papers), Medical Imaging Techniques and Applications (8 papers), Breast Cancer Treatment Studies (7 papers), Digital Radiography and Breast Imaging (6 papers), Gene expression and cancer classification (4 papers) and Genomics and Phylogenetic Studies (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (819 citations), Health Informatics (50 citations), Artificial Intelligence (379 citations), Genetics (90 citations) and Cancer Research (114 citations). Ming Fan has collaborated with scholars based in China, Saudi Arabia and United States. Frequent co-authors include Lihua Li, Xin Gao, Juan Zhang, Yu Li, Ramzan Kh. Umarov, Cheng Hu, Peng hui Zhang, Shiwei Wang, Bingqing Xie and Sheng Wang. Their work appears in journals such as Physics in Medicine and Biology, Bioinformatics, IEEE Journal of Biomedical and Health Informatics, Journal of Translational Medicine and NeuroImage.
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.