Jun Cheng
Impact in
-
- Radiomics and Machine Learning in Medical Imaging
- Health Informatics top 10%
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 16
- Oncology 14
- Colorectal Cancer Treatments and Studies 6
- Pancreatic and Hepatic Oncology Research 4
- Co-authors
- Kun Huang (14 shared papers)Jie Zhang (10 shared papers)Zhi Han (7 shared papers)Dong Ni (16 shared papers)Anil V. Parwani (2 shared papers)Qianjin Feng (3 shared papers)Andrew I. Brooks (1 shared paper)E L Jacobson (1 shared paper)
- Journals
- Information Sciences (2 papers)Communications in Nonlinear Science and Numerical Simulation (2 papers)Medical Physics (2 papers)Frontiers in Genetics (2 papers)Fuzzy Sets and Systems (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jun Cheng
70 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 106
- Radiology, Nuclear Medicine and Imaging 275
- Health Informatics 16
- Cancer Research 149
- Artificial Intelligence 294
- Cellular and Molecular Neuroscience 135
Countries citing papers authored by Jun Cheng
This map shows the geographic impact of Jun Cheng'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 Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Cheng more than expected).
Fields of papers citing papers by Jun Cheng
This network shows the impact of papers produced by Jun Cheng. 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 Cheng. The network helps show where Jun Cheng may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Cheng, 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 85 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1995 | 139 | |
| 2 | 2017 | 104 | |
| 3 | 2022 | 88 | |
| 4 | 2019 | 79 | |
| 5 | 2020 | 64 | |
| 6 | 2020 | 63 | |
| 7 | 2017 | 61 | |
| 8 | 2016 | 49 | |
| 9 | 2022 | 44 | |
| 10 | 2012 | 35 | |
| 11 | 1992 | 28 | |
| 12 | 2019 | 24 | |
| 13 | 2018 | 23 | |
| 14 | 2020 | 20 | |
| 15 | 2019 | 19 | |
| 16 | 2019 | 19 | |
| 17 | 2018 | 18 | |
| 18 | 2018 | 15 | |
| 19 | 2023 | 14 | |
| 20 | 2019 | 13 |
About Jun Cheng
Jun Cheng is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology, Molecular Biology, Artificial Intelligence and Cancer Research, having authored 85 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (16 papers), AI in cancer detection (9 papers), Stability and Control of Uncertain Systems (7 papers), Cancer Genomics and Diagnostics (7 papers), Colorectal Cancer Treatments and Studies (6 papers), Hepatitis C virus research (4 papers), Fault Detection and Control Systems (4 papers) and Pancreatic and Hepatic Oncology Research (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (275 citations), Health Informatics (16 citations), Cancer Research (149 citations), Artificial Intelligence (294 citations) and Cellular and Molecular Neuroscience (135 citations). Jun Cheng has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Kun Huang, Jie Zhang, Zhi Han, Dong Ni, Anil V. Parwani, Qianjin Feng, Andrew I. Brooks, E L Jacobson, Kelly M. Standifer and Liang Cheng. Their work appears in journals such as Information Sciences, Communications in Nonlinear Science and Numerical Simulation, Medical Physics, Frontiers in Genetics and Fuzzy Sets and Systems.
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.