Kaidong Li
Impact in
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- Radiomics and Machine Learning in Medical Imaging
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- Polysaccharides and Plant Cell Walls
- Plant Virus Research Studies
Papers in
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- Advanced Neural Network Applications 7
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- Domain Adaptation and Few-Shot Learning 4
- Adversarial Robustness in Machine Learning 2
- Co-authors
- Guanghui Wang (10 shared papers)Xiangdong Gao (5 shared papers)Wenbing Yao (2 shared papers)Shuai Tang (2 shared papers)Wei Liu (3 shared papers)Krushi Patel (2 shared papers)Ajay Bansal (2 shared papers)Amit Rastogi (2 shared papers)
- Journals
- Journal of Ethnopharmacology (2 papers)Journal of Functional Foods (1 paper)Journal of Biosciences (1 paper)Carbohydrate Polymers (1 paper)PLoS ONE (1 paper)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Kaidong Li
20 papers receiving 480 citations
Peers
Comparison fields: 5 of 101
- Radiology, Nuclear Medicine and Imaging 74
- Plant Science 128
- Oncology 84
- Computer Vision and Pattern Recognition 70
- Pharmacology 26
Countries citing papers authored by Kaidong Li
This map shows the geographic impact of Kaidong Li'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 Kaidong Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaidong Li more than expected).
Fields of papers citing papers by Kaidong Li
This network shows the impact of papers produced by Kaidong Li. 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 Kaidong Li. The network helps show where Kaidong Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Kaidong Li, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 113 | |
| 2 | 2021 | 86 | |
| 3 | 2020 | 61 | |
| 4 | 2019 | 49 | |
| 5 | 2011 | 33 | |
| 6 | 2020 | 26 | |
| 7 | 2022 | 17 | |
| 8 | 2020 | 16 | |
| 9 | 2021 | 14 | |
| 10 | 2021 | 13 | |
| 11 | 2023 | 12 | |
| 12 | 2020 | 12 | |
| 13 | 2023 | 9 | |
| 14 | 2013 | 8 | |
| 15 | 2023 | 4 | |
| 16 | 2024 | 3 | |
| 17 | 2020 | 3 | |
| 18 | 2025 | 2 | |
| 19 | 2024 | 1 | |
| 20 | 2024 | 1 |
About Kaidong Li
Kaidong Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Molecular Biology and Public Health, Environmental and Occupational Health, having authored 22 papers that have together received 483 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (7 papers), Domain Adaptation and Few-Shot Learning (4 papers), Mosquito-borne diseases and control (3 papers), Adversarial Robustness in Machine Learning (2 papers), Polysaccharides and Plant Cell Walls (2 papers), Colorectal Cancer Screening and Detection (2 papers), 3D Shape Modeling and Analysis (2 papers) and Date Palm Research Studies (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (74 citations), Plant Science (128 citations), Oncology (84 citations), Computer Vision and Pattern Recognition (70 citations) and Pharmacology (26 citations). Kaidong Li has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Guanghui Wang, Xiangdong Gao, Wenbing Yao, Shuai Tang, Wei Liu, Krushi Patel, Ajay Bansal, Amit Rastogi, Jingyi Zhu and Cuncong Zhong. Their work appears in journals such as Journal of Ethnopharmacology, Journal of Functional Foods, Journal of Biosciences, Carbohydrate Polymers and PLoS ONE.
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.