Biting Yu
Impact in
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- Medical Image Segmentation Techniques
- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Advanced Neural Network Applications
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- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Image and Signal Denoising Methods 5
- Advanced Image Processing Techniques 5
- Generative Adversarial Networks and Image Synthesis 5
- Medical Image Segmentation Techniques 4
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- Advanced Clustering Algorithms Research 2
- Co-authors
- Luping Zhou (11 shared papers)Lei Wang (9 shared papers)Yan Wang (5 shared papers)Dinggang Shen (4 shared papers)Jürgen Fripp (4 shared papers)Pierrick Bourgeat (4 shared papers)Weili Lin (3 shared papers)Xi Wu (4 shared papers)
In The Last Decade
Biting Yu
15 papers receiving 980 citations
Biting Yu's Hit Papers
Peers
Comparison fields: 5 of 82
- Computer Vision and Pattern Recognition 516
- Radiology, Nuclear Medicine and Imaging 481
- Media Technology 141
- Neurology 115
- Biophysics 66
Countries citing papers authored by Biting Yu
This map shows the geographic impact of Biting Yu'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 Biting Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Biting Yu more than expected).
Fields of papers citing papers by Biting Yu
This network shows the impact of papers produced by Biting Yu. 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 Biting Yu. The network helps show where Biting Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Biting Yu, 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 | 3D conditional generative adversarial networks for high-quality PET image estimation at low dose Hit paper breakdown → | 2018 | 321 |
| 2 | 2019 | 184 | |
| 3 | 2018 | 155 | |
| 4 | 2018 | 74 | |
| 5 | 2015 | 72 | |
| 6 | 2020 | 45 | |
| 7 | 2021 | 32 | |
| 8 | 2020 | 26 | |
| 9 | 2018 | 23 | |
| 10 | 2016 | 21 | |
| 11 | 2021 | 15 | |
| 12 | 2016 | 15 | |
| 13 | 2016 | 7 | |
| 14 | 2019 | 5 | |
| 15 | 2018 | 3 |
About Biting Yu
Biting Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Media Technology and Neurology, having authored 15 papers that have together received 998 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (5 papers), Advanced Image Processing Techniques (5 papers), Generative Adversarial Networks and Image Synthesis (5 papers), Medical Image Segmentation Techniques (4 papers), Medical Imaging Techniques and Applications (3 papers), Image Processing Techniques and Applications (2 papers), Brain Tumor Detection and Classification (2 papers) and Advanced Clustering Algorithms Research (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (516 citations), Radiology, Nuclear Medicine and Imaging (481 citations), Media Technology (141 citations), Neurology (115 citations) and Biophysics (66 citations). Biting Yu has collaborated with scholars based in China, Australia and Hong Kong. Frequent co-authors include Luping Zhou, Lei Wang, Yan Wang, Dinggang Shen, Jürgen Fripp, Pierrick Bourgeat, Weili Lin, Xi Wu, David S. Lalush and Chen Zu. Their work appears in journals such as IEEE Transactions on Medical Imaging, Neurocomputing, Advances in experimental medicine and biology, Knowledge-Based Systems and NeuroImage.
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.