Chung‐Ming Chen
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Health Informatics top 2%
Papers in
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- Radiomics and Machine Learning in Medical Imaging 26
- Medical Imaging Techniques and Applications 14
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- Medical Image Segmentation Techniques 17
- Image and Signal Denoising Methods 7
- Co-authors
- Yi‐Hong ChouYeun‐Chung ChangDinggang ShenChiun‐Sheng HuangJie-Zhi ChengDong NiJing QinChui-Mei Tiu
- Journals
- Scientific Reports (7 papers)Ultrasound in Medicine & Biology (5 papers)Annals of Surgical Oncology (4 papers)Cancers (3 papers)IEEE Access (2 papers)
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Chung‐Ming Chen
122 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Radiology, Nuclear Medicine and Imaging 1.1k
- Health Informatics 60
- Computer Vision and Pattern Recognition 452
- Artificial Intelligence 652
- Pulmonary and Respiratory Medicine 470
Countries citing papers authored by Chung‐Ming Chen
This map shows the geographic impact of Chung‐Ming 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 Chung‐Ming Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chung‐Ming Chen more than expected).
Fields of papers citing papers by Chung‐Ming Chen
This network shows the impact of papers produced by Chung‐Ming 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 Chung‐Ming Chen. The network helps show where Chung‐Ming Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Chung‐Ming 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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 1 | |
| 6 | 2019 | 2 | |
| 7 | 2018 | 24 | |
| 8 | 2018 | 15 | |
| 9 | 2015 | 6 | |
| 10 | 2015 | 18 | |
| 11 | 2014 | 6 | |
| 12 | 2014 | 17 | |
| 13 | 2012 | 47 | |
| 14 | 2008 | 46 | |
| 15 | 2008 | 13 | |
| 16 | 2007 | 12 | |
| 17 | 2005 | 35 | |
| 18 | 2004 | 136 | |
| 19 | 2002 | 22 | |
| 20 | 1998 | 10 |
About Chung‐Ming Chen
Chung‐Ming Chen is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Oral Surgery and Media Technology, having authored 127 papers that have together received 2.5k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (26 papers), Lung Cancer Diagnosis and Treatment (23 papers), Medical Image Segmentation Techniques (17 papers), AI in cancer detection (16 papers), Medical Imaging Techniques and Applications (14 papers), Bioinformatics and Genomic Networks (9 papers), Advanced X-ray and CT Imaging (8 papers) and Image and Signal Denoising Methods (7 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.1k citations), Health Informatics (60 citations), Computer Vision and Pattern Recognition (452 citations), Artificial Intelligence (652 citations) and Pulmonary and Respiratory Medicine (470 citations). Chung‐Ming Chen has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Yi‐Hong Chou, Yeun‐Chung Chang, Dinggang Shen, Chiun‐Sheng Huang, Jie-Zhi Cheng, Dong Ni, Jing Qin, Chui-Mei Tiu, Jung‐Hsin Lin and Chui‐Mei Tiu. Their work appears in journals such as Scientific Reports, Ultrasound in Medicine & Biology, Annals of Surgical Oncology, Cancers and IEEE Access.
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.