Ming‐De Li
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 12
- MRI in cancer diagnosis 4
- Hepatology 10
- Hepatocellular Carcinoma Treatment and Prognosis 9
- Co-authors
- Wei Wang (19 shared papers)Ming‐De Lu (12 shared papers)Li‐Da Chen (14 shared papers)Si‐Min Ruan (13 shared papers)Ming Kuang (12 shared papers)Wenjuan Tong (4 shared papers)Shaohua Chen (1 shared paper)Han Xiao (2 shared papers)
- Journals
- European Radiology (4 papers)Radiology (2 papers)Chinese Journal of Electrical Engineering (1 paper)JAMA Network Open (1 paper)Nature Communications (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Ming‐De Li
25 papers receiving 217 citations
Peers
Comparison fields: 5 of 76
- Health Informatics 44
- Radiology, Nuclear Medicine and Imaging 121
- Hepatology 37
- Artificial Intelligence 46
- Modeling and Simulation 5
Countries citing papers authored by Ming‐De Li
This map shows the geographic impact of Ming‐De Li'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‐De Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐De Li more than expected).
Fields of papers citing papers by Ming‐De Li
This network shows the impact of papers produced by Ming‐De Li. 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‐De Li. The network helps show where Ming‐De Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming‐De Li, 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 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 42 | |
| 2 | 2023 | 29 | |
| 3 | 2022 | 18 | |
| 4 | 1995 | 14 | |
| 5 | 2022 | 14 | |
| 6 | 2022 | 13 | |
| 7 | 2021 | 13 | |
| 8 | 2024 | 12 | |
| 9 | 2023 | 11 | |
| 10 | 2021 | 11 | |
| 11 | 2024 | 8 | |
| 12 | 2019 | 7 | |
| 13 | 2021 | 7 | |
| 14 | 2024 | 5 | |
| 15 | 2023 | 5 | |
| 16 | 2023 | 4 | |
| 17 | 2025 | 4 | |
| 18 | 2021 | 3 | |
| 19 | 2023 | 2 | |
| 20 | 2024 | 1 |
About Ming‐De Li
Ming‐De Li is a scholar working on Radiology, Nuclear Medicine and Imaging, Hepatology, Artificial Intelligence, Epidemiology and Computer Vision and Pattern Recognition, having authored 28 papers that have together received 228 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (12 papers), Hepatocellular Carcinoma Treatment and Prognosis (9 papers), AI in cancer detection (5 papers), Liver Disease Diagnosis and Treatment (5 papers), MRI in cancer diagnosis (4 papers), Artificial Intelligence in Healthcare and Education (2 papers), Advanced X-ray and CT Imaging (2 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (2 papers). The work is most often cited by research in Health Informatics (44 citations), Radiology, Nuclear Medicine and Imaging (121 citations), Hepatology (37 citations), Artificial Intelligence (46 citations) and Modeling and Simulation (5 citations). Ming‐De Li has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Wei Wang, Ming‐De Lu, Li‐Da Chen, Si‐Min Ruan, Ming Kuang, Wenjuan Tong, Shaohua Chen, Han Xiao, Xiaoyan Xie and Hui Huang. Their work appears in journals such as European Radiology, Radiology, Chinese Journal of Electrical Engineering, JAMA Network Open and Nature Communications.
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.