Pingjun Chen
Impact in
- Health Informatics top 5%
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Infrared Thermography in Medicine
Papers in
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- Radiomics and Machine Learning in Medical Imaging 12
- COVID-19 diagnosis using AI 2
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- AI in cancer detection 12
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Lin Yang (10 shared papers)Xiaoshuang Shi (8 shared papers)Linlin Gao (1 shared paper)Kyle D. Allen (1 shared paper)Zizhao Zhang (4 shared papers)Manish Sapkota (2 shared papers)Fuyong Xing (4 shared papers)Jinzheng Cai (3 shared papers)
- Journals
- Cancers (4 papers)IEEE Transactions on Image Processing (2 papers)Nature Communications (2 papers)Modern Pathology (2 papers)Artificial Intelligence in Medicine (1 paper)
- Partner nations
- United StatesChinaPakistan
In The Last Decade
Pingjun Chen
33 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 94
- Health Informatics 52
- Radiology, Nuclear Medicine and Imaging 398
- Artificial Intelligence 419
- Rheumatology 159
- Biophysics 63
Countries citing papers authored by Pingjun Chen
This map shows the geographic impact of Pingjun Chen'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 Pingjun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pingjun Chen more than expected).
Fields of papers citing papers by Pingjun Chen
This network shows the impact of papers produced by Pingjun Chen. 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 Pingjun Chen. The network helps show where Pingjun Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Pingjun Chen, 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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 250 | |
| 2 | 2019 | 223 | |
| 3 | 2023 | 81 | |
| 4 | 2017 | 56 | |
| 5 | 2024 | 50 | |
| 6 | 2023 | 45 | |
| 7 | 2018 | 43 | |
| 8 | 2020 | 35 | |
| 9 | 2018 | 34 | |
| 10 | 2019 | 32 | |
| 11 | 2020 | 32 | |
| 12 | 2020 | 31 | |
| 13 | 2019 | 30 | |
| 14 | 2021 | 25 | |
| 15 | 2023 | 22 | |
| 16 | 2021 | 20 | |
| 17 | 2024 | 20 | |
| 18 | 2025 | 19 | |
| 19 | 2024 | 18 | |
| 20 | 2022 | 13 |
About Pingjun Chen
Pingjun Chen is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition and Biophysics, having authored 35 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (12 papers), AI in cancer detection (12 papers), Lung Cancer Diagnosis and Treatment (5 papers), Cell Image Analysis Techniques (3 papers), Chronic Lymphocytic Leukemia Research (3 papers), Cancer Genomics and Diagnostics (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Health Informatics (52 citations), Radiology, Nuclear Medicine and Imaging (398 citations), Artificial Intelligence (419 citations), Rheumatology (159 citations) and Biophysics (63 citations). Pingjun Chen has collaborated with scholars based in United States, China and Pakistan. Frequent co-authors include Lin Yang, Xiaoshuang Shi, Linlin Gao, Kyle D. Allen, Zizhao Zhang, Manish Sapkota, Fuyong Xing, Jinzheng Cai, Hai Tao Su and Yuanpu Xie. Their work appears in journals such as Cancers, IEEE Transactions on Image Processing, Nature Communications, Modern Pathology and Artificial Intelligence in Medicine.
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.