Chong Ma
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Radiology practices and education
- Retinal Imaging and Analysis
Papers in
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- Topic Modeling 4
- AI in cancer detection 2
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- Visual Attention and Saliency Detection 2
- Co-authors
- Tuo Zhang (8 shared papers)Xi Jiang (5 shared papers)Dinggang Shen (7 shared papers)Jiaqi Wang (2 shared papers)Tianming Liu (7 shared papers)Lei Guo (3 shared papers)Bao Ge (1 shared paper)Enze Shi (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (2 papers)IEEE Transactions on Neural Networks and Learning Systems (2 papers)Nature Communications (1 paper)Pattern Recognition (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Chong Ma
15 papers receiving 145 citations
Peers
Comparison fields: 5 of 55
- Health Informatics 20
- Radiology, Nuclear Medicine and Imaging 36
- Artificial Intelligence 46
- Human-Computer Interaction 7
- Computer Vision and Pattern Recognition 17
Countries citing papers authored by Chong Ma
This map shows the geographic impact of Chong Ma'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 Chong Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chong Ma more than expected).
Fields of papers citing papers by Chong Ma
This network shows the impact of papers produced by Chong Ma. 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 Chong Ma. The network helps show where Chong Ma may publish in the future.
Co-authors
The 25 scholars most cited alongside Chong Ma, 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 | 2024 | 47 | |
| 2 | 2024 | 36 | |
| 3 | 2023 | 24 | |
| 4 | 2023 | 11 | |
| 5 | 2022 | 7 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 4 | |
| 8 | 2025 | 3 | |
| 9 | 2025 | 2 | |
| 10 | 2025 | 2 | |
| 11 | Principles and Validation of Unified Approach for Nested Overlap of Geological Variable Variograms | 2010 | 1 |
| 12 | On a Robust Variogram Estimator | 2009 | 1 |
| 13 | 2025 | 1 | |
| 14 | 2024 | 1 | |
| 15 | 2023 | 1 | |
| 16 | 2009 | 0 | |
| 17 | 2024 | 0 | |
| 18 | 2023 | 0 |
About Chong Ma
Chong Ma is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Economics and Econometrics, having authored 18 papers that have together received 145 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Regional Economic and Spatial Analysis (2 papers), Remote Sensing and Land Use (2 papers), Visual Attention and Saliency Detection (2 papers), AI in cancer detection (2 papers), Ferroelectric and Negative Capacitance Devices (1 paper), Brain Tumor Detection and Classification (1 paper) and Advanced Causal Inference Techniques (1 paper). The work is most often cited by research in Health Informatics (20 citations), Radiology, Nuclear Medicine and Imaging (36 citations), Artificial Intelligence (46 citations), Human-Computer Interaction (7 citations) and Computer Vision and Pattern Recognition (17 citations). Chong Ma has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Tuo Zhang, Xi Jiang, Dinggang Shen, Jiaqi Wang, Tianming Liu, Lei Guo, Bao Ge, Enze Shi, Yiheng Liu and Xuhui Wang. Their work appears in journals such as IEEE Transactions on Medical Imaging, IEEE Transactions on Neural Networks and Learning Systems, Nature Communications, Pattern Recognition and IEEE Journal of Biomedical and Health Informatics.
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.