Kaiyang Cheng
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Aging top 5%
- Genetics, Aging, and Longevity in Model Organisms
Papers in
-
- Artificial Intelligence in Healthcare and Education 3
- Aging 1
- Genetics, Aging, and Longevity in Model Organisms 1
- Co-authors
- Valentina Pedoia (4 shared papers)Sharmila Majumdar (3 shared papers)Justin D. Krogue (2 shared papers)Eugene Ozhinsky (1 shared paper)Kevin Hwang (1 shared paper)Paul A. Toogood (1 shared paper)Jae Ho Sohn (1 shared paper)Bryan Darger (1 shared paper)
- Journals
- Radiology Artificial Intelligence (1 paper)GeroScience (1 paper)Science (1 paper)Lecture notes in computer science (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesItalyCanada
In The Last Decade
Kaiyang Cheng
7 papers receiving 293 citations
Peers
Comparison fields: 5 of 55
- Health Informatics 104
- Aging 42
- Biomedical Engineering 78
- Neuropsychology and Physiological Psychology 5
- Molecular Biology 77
Countries citing papers authored by Kaiyang Cheng
This map shows the geographic impact of Kaiyang Cheng'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 Kaiyang Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaiyang Cheng more than expected).
Fields of papers citing papers by Kaiyang Cheng
This network shows the impact of papers produced by Kaiyang Cheng. 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 Kaiyang Cheng. The network helps show where Kaiyang Cheng may publish in the future.
Co-authors
The 16 scholars most cited alongside Kaiyang Cheng, 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 | 2020 | 144 | |
| 2 | 2023 | 77 | |
| 3 | 2024 | 28 | |
| 4 | 2023 | 22 | |
| 5 | 2019 | 15 | |
| 6 | Addressing The False Negative Problem of Deep Learning MRI Reconstruction Models by Adversarial Attacks and Robust Training | 2020 | 10 |
| 7 | 2021 | 4 |
About Kaiyang Cheng
Kaiyang Cheng is a scholar working on Health Informatics, Aging, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 300 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (3 papers), Medical Imaging and Analysis (2 papers), Epigenetics and DNA Methylation (2 papers), Advanced MRI Techniques and Applications (2 papers), Hip and Femur Fractures (1 paper), Genetics, Aging, and Longevity in Model Organisms (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Health Informatics (104 citations), Aging (42 citations), Biomedical Engineering (78 citations), Neuropsychology and Physiological Psychology (5 citations) and Molecular Biology (77 citations). Kaiyang Cheng has collaborated with scholars based in United States, Italy and Canada. Frequent co-authors include Valentina Pedoia, Sharmila Majumdar, Justin D. Krogue, Eugene Ozhinsky, Kevin Hwang, Paul A. Toogood, Jae Ho Sohn, Bryan Darger, Musa B. Zaid and Rina Patel. Their work appears in journals such as Radiology Artificial Intelligence, GeroScience, Science, Lecture notes in computer science and arXiv (Cornell University).
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.