Ge Wang
Impact in
- Radiology, Nuclear Medicine and Imaging top 0.01%
- Medical Imaging Techniques and Applications
- Radiation Dose and Imaging
- Advanced MRI Techniques and Applications
- Optical Imaging and Spectroscopy Techniques
- Radiomics and Machine Learning in Medical Imaging
- Health Informatics top 0.1%
Papers in
-
- Medical Imaging Techniques and Applications 388
- Radiation Dose and Imaging 114
- Advanced MRI Techniques and Applications 111
- Optical Imaging and Spectroscopy Techniques 74
- Radiomics and Machine Learning in Medical Imaging 55
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- Advanced X-ray and CT Imaging 287
- Photoacoustic and Ultrasonic Imaging 80
- Co-authors
- Hengyong Yu (129 shared papers)Frank Natterer (1 shared paper)Michael W. Vannier (62 shared papers)Ming Jiang (35 shared papers)Yi Zhang (19 shared papers)Mannudeep K. Kalra (27 shared papers)Malcolm Slaney (1 shared paper)Avinash C. Kak (1 shared paper)
- Journals
- Medical Physics (61 papers)Journal of X-Ray Science and Technology (54 papers)IEEE Transactions on Medical Imaging (45 papers)Physics in Medicine and Biology (39 papers)IEEE Access (20 papers)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Ge Wang
959 papers receiving 28.2k citations
Ge Wang's Hit Papers
Peers
Comparison fields: 5 of 229
- Radiology, Nuclear Medicine and Imaging 15.3k
- Health Informatics 417
- Biomedical Engineering 12.7k
- Radiation 2.4k
- Computer Vision and Pattern Recognition 4.2k
Countries citing papers authored by Ge Wang
This map shows the geographic impact of Ge Wang'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 Ge Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ge Wang more than expected).
Fields of papers citing papers by Ge Wang
This network shows the impact of papers produced by Ge Wang. 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 Ge Wang. The network helps show where Ge Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Ge Wang, 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 1.0k papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The Mathematics of Computerized Tomography Hit paper breakdown → | 2002 | 1352 |
| 2 | Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network Hit paper breakdown → | 2017 | 1249 |
| 3 | Principles of Computerized Tomographic Imaging Hit paper breakdown → | 2002 | 1160 |
| 4 | Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss Hit paper breakdown → | 2018 | 1103 |
| 5 | aLow-dose CT via convolutional neural network Hit paper breakdown → | 2017 | 523 |
| 6 | Low-Dose X-ray CT Reconstruction via Dictionary Learning Hit paper breakdown → | 2012 | 501 |
| 7 | Compressed sensing based interior tomography Hit paper breakdown → | 2009 | 400 |
| 8 | Deep learning for tomographic image reconstruction Hit paper breakdown → | 2020 | 371 |
| 9 | On Interpretability of Artificial Neural Networks: A Survey Hit paper breakdown → | 2021 | 334 |
| 10 | Image Reconstruction is a New Frontier of Machine Learning Hit paper breakdown → | 2018 | 324 |
| 11 | A Perspective on Deep Imaging Hit paper breakdown → | 2016 | 318 |
| 12 | 1996 | 313 | |
| 13 | LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT Hit paper breakdown → | 2018 | 308 |
| 14 | Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction Hit paper breakdown → | 2019 | 290 |
| 15 | 1993 | 255 | |
| 16 | 2013 | 245 | |
| 17 | Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential Hit paper breakdown → | 2023 | 242 |
| 18 | 2005 | 238 | |
| 19 | 2019 | 227 | |
| 20 | 2004 | 226 |
About Ge Wang
Ge Wang is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Computer Vision and Pattern Recognition, Radiation and Molecular Biology, having authored 1.0k papers that have together received 29.3k indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (388 papers), Advanced X-ray and CT Imaging (287 papers), Radiation Dose and Imaging (114 papers), Advanced MRI Techniques and Applications (111 papers), Photoacoustic and Ultrasonic Imaging (80 papers), Optical Imaging and Spectroscopy Techniques (74 papers), Advanced X-ray Imaging Techniques (62 papers) and Radiomics and Machine Learning in Medical Imaging (55 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (15.3k citations), Health Informatics (417 citations), Biomedical Engineering (12.7k citations), Radiation (2.4k citations) and Computer Vision and Pattern Recognition (4.2k citations). Ge Wang has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Hengyong Yu, Frank Natterer, Michael W. Vannier, Ming Jiang, Yi Zhang, Mannudeep K. Kalra, Malcolm Slaney, Avinash C. Kak, Bruno De Man and Jiliu Zhou. Their work appears in journals such as Medical Physics, Journal of X-Ray Science and Technology, IEEE Transactions on Medical Imaging, Physics in Medicine and Biology 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.