Cheng Jin
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Radiomics and Machine Learning in Medical Imaging 8
- COVID-19 diagnosis using AI 3
-
- Advanced X-ray and CT Imaging 3
- Co-authors
- Lei Deng (1 shared paper)Weixiang Chen (1 shared paper)Jie Zhou (2 shared papers)Zimeng Tan (1 shared paper)Zhanwei Xu (1 shared paper)Xin Zhang (1 shared paper)Chuansheng Zheng (1 shared paper)Jianjiang Feng (2 shared papers)
- Journals
- Nature Communications (2 papers)Frontiers in Oncology (2 papers)European Journal of Radiology (2 papers)Medical Physics (1 paper)Clinical & Experimental Metastasis (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Cheng Jin
31 papers receiving 929 citations
Peers
Comparison fields: 5 of 108
- Health Informatics 99
- Radiology, Nuclear Medicine and Imaging 481
- Hepatology 72
- Health Information Management 29
- Artificial Intelligence 192
Countries citing papers authored by Cheng Jin
This map shows the geographic impact of Cheng Jin'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 Cheng Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cheng Jin more than expected).
Fields of papers citing papers by Cheng Jin
This network shows the impact of papers produced by Cheng Jin. 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 Cheng Jin. The network helps show where Cheng Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Cheng Jin, 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 347 | |
| 2 | 2021 | 142 | |
| 3 | 2022 | 54 | |
| 4 | 2020 | 52 | |
| 5 | 2019 | 51 | |
| 6 | 2020 | 49 | |
| 7 | 2020 | 38 | |
| 8 | 2016 | 21 | |
| 9 | 2021 | 18 | |
| 10 | 2022 | 18 | |
| 11 | 2021 | 16 | |
| 12 | 2025 | 16 | |
| 13 | 2020 | 14 | |
| 14 | 2021 | 13 | |
| 15 | 2023 | 11 | |
| 16 | 2020 | 11 | |
| 17 | 2021 | 11 | |
| 18 | 2019 | 10 | |
| 19 | 2022 | 10 | |
| 20 | 2021 | 7 |
About Cheng Jin
Cheng Jin is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Materials Chemistry, Molecular Biology and Pulmonary and Respiratory Medicine, having authored 34 papers that have together received 944 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), Advanced X-ray and CT Imaging (3 papers), Carbon and Quantum Dots Applications (3 papers), COVID-19 diagnosis using AI (3 papers), Hepatocellular Carcinoma Treatment and Prognosis (3 papers), AI in cancer detection (2 papers), Brain Tumor Detection and Classification (2 papers) and Nanoparticles: synthesis and applications (2 papers). The work is most often cited by research in Health Informatics (99 citations), Radiology, Nuclear Medicine and Imaging (481 citations), Hepatology (72 citations), Health Information Management (29 citations) and Artificial Intelligence (192 citations). Cheng Jin has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Lei Deng, Weixiang Chen, Jie Zhou, Zimeng Tan, Zhanwei Xu, Xin Zhang, Chuansheng Zheng, Jianjiang Feng, Yukun Cao and Heshui Shi. Their work appears in journals such as Nature Communications, Frontiers in Oncology, European Journal of Radiology, Medical Physics and Clinical & Experimental Metastasis.
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.