Hui Cui
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in ⓘ
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- Radiomics and Machine Learning in Medical Imaging 32
- Medical Imaging Techniques and Applications 10
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- Computational Drug Discovery Methods 16
- Co-authors
- Ping Xuan (39 shared papers)Tiangang Zhang (36 shared papers)Xiuying Wang (17 shared papers)Qiangguo Jin (15 shared papers)Ran Su (7 shared papers)Changming Sun (6 shared papers)Zhaopeng Meng (3 shared papers)Dagan Feng (15 shared papers)
In The Last Decade
Hui Cui
88 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 108
- Radiology, Nuclear Medicine and Imaging 313
- Computer Vision and Pattern Recognition 248
- Artificial Intelligence 356
- Health Informatics 15
- Computational Theory and Mathematics 152
Countries citing papers authored by Hui Cui
This map shows the geographic impact of Hui Cui'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 Hui Cui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hui Cui more than expected).
Fields of papers citing papers by Hui Cui
This network shows the impact of papers produced by Hui Cui. 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 Hui Cui. The network helps show where Hui Cui may publish in the future.
Co-authors
The 25 scholars most cited alongside Hui Cui, 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 97 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 89 | |
| 2 | 2021 | 70 | |
| 3 | 2019 | 62 | |
| 4 | 2017 | 56 | |
| 5 | 2021 | 55 | |
| 6 | 2020 | 50 | |
| 7 | 2021 | 42 | |
| 8 | 2021 | 39 | |
| 9 | 2021 | 28 | |
| 10 | 2021 | 25 | |
| 11 | 2023 | 24 | |
| 12 | 2019 | 24 | |
| 13 | 2015 | 21 | |
| 14 | 2019 | 20 | |
| 15 | 2019 | 19 | |
| 16 | 2017 | 19 | |
| 17 | 2022 | 17 | |
| 18 | 2018 | 17 | |
| 19 | 2022 | 16 | |
| 20 | 2022 | 16 |
About Hui Cui
Hui Cui is a scholar working on Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Toxicology and Artificial Intelligence, having authored 97 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (32 papers), Computational Drug Discovery Methods (16 papers), AI in cancer detection (13 papers), Bioinformatics and Genomic Networks (12 papers), Cancer-related molecular mechanisms research (11 papers), Lung Cancer Diagnosis and Treatment (11 papers), Medical Imaging Techniques and Applications (10 papers) and Medical Image Segmentation Techniques (9 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (313 citations), Computer Vision and Pattern Recognition (248 citations), Artificial Intelligence (356 citations), Health Informatics (15 citations) and Computational Theory and Mathematics (152 citations). Hui Cui has collaborated with scholars based in China, Australia and Japan. Frequent co-authors include Ping Xuan, Tiangang Zhang, Xiuying Wang, Qiangguo Jin, Ran Su, Changming Sun, Zhaopeng Meng, Dagan Feng, Toshiya Nakaguchi and Nan Sheng. Their work appears in journals such as Briefings in Bioinformatics, Journal of Chemical Information and Modeling, Physics in Medicine and Biology, Knowledge-Based Systems and Applied Soft Computing.
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.