Jun Cheng
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- Radiomics and Machine Learning in Medical Imaging 16
- Health Informatics top 10%
- Cancer Research top 10%
- Cancer Genomics and Diagnostics 8
- Artificial Intelligence top 5%
- AI in cancer detection 8
- Biophysics top 10%
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- Colorectal Cancer Treatments and Studies 7
- Pancreatic and Hepatic Oncology Research 5
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- Hepatitis C virus research 7
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- Genetic factors in colorectal cancer 4
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- Stability and Control of Uncertain Systems 4
- Co-authors
- Kun HuangJie ZhangZhi HanDong NiAnil V. ParwaniQianjin FengLiang ChengE L Jacobson
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Jun Cheng
59 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 109
- Radiology, Nuclear Medicine and Imaging 324
- Health Informatics 17
- Cancer Research 178
- Artificial Intelligence 276
- Biophysics 44
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 0 | |
| 6 | 2025 | 0 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 0 | |
| 9 | 2023 | 10 | |
| 10 | 2022 | 11 | |
| 11 | 2022 | 68 | |
| 12 | 2022 | 5 | |
| 13 | 2021 | 11 | |
| 14 | 2020 | 20 | |
| 15 | 2020 | 58 | |
| 16 | 2019 | 19 | |
| 17 | Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer | 2019 | 1 |
| 18 | 2018 | 15 | |
| 19 | 2017 | 98 | |
| 20 | 1995 | 129 |
About Jun Cheng
Jun Cheng is a scholar working on Hepatology, Radiology, Nuclear Medicine and Imaging, Cancer Research, Health Informatics and Oncology, having authored 73 papers that have together received 1.0k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (16 papers), Cancer Genomics and Diagnostics (8 papers), AI in cancer detection (8 papers), Colorectal Cancer Treatments and Studies (7 papers), Hepatitis C virus research (7 papers), Pancreatic and Hepatic Oncology Research (5 papers), Genetic factors in colorectal cancer (4 papers) and Stability and Control of Uncertain Systems (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (324 citations), Health Informatics (17 citations), Cancer Research (178 citations), Artificial Intelligence (276 citations) and Biophysics (44 citations). Jun Cheng has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Kun Huang, Jie Zhang, Zhi Han, Dong Ni, Anil V. Parwani, Qianjin Feng, Liang Cheng, E L Jacobson, Andrew I. Brooks and Greg Dean. Their work appears in journals such as Information Sciences, Medical Physics, Cancer Science, Frontiers in Genetics and Radiology Artificial Intelligence.
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.