Chengkuan Chen
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Health Informatics top 1%
- Molecular Biology
- Co-authors
- Faisal MahmoodTiffany ChenRichard J. ChenMing Y. LuDrew F. K. WilliamsonAndrew D. TristerRahul G. KrishnanYicong Li
- Topics
- AI in cancer detection (4 papers)Radiomics and Machine Learning in Medical Imaging (2 papers)Image Retrieval and Classification Techniques (1 paper)
- Partner nations
- United StatesCanadaSouth Korea
In The Last Decade
Chengkuan Chen
5 papers receiving 794 citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Artificial Intelligence 488
- Radiology, Nuclear Medicine and Imaging 372
- Computer Vision and Pattern Recognition 177
- Health Informatics 120
- Molecular Biology 113
Countries citing papers authored by Chengkuan Chen
This map shows the geographic impact of Chengkuan Chen'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 Chengkuan Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chengkuan Chen more than expected).
Fields of papers citing papers by Chengkuan Chen
This network shows the impact of papers produced by Chengkuan Chen. 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 Chengkuan Chen. The network helps show where Chengkuan Chen may publish in the future.
Co-authorship network of co-authors of Chengkuan Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Chengkuan Chen. A scholar is included among the top collaborators of Chengkuan Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chengkuan Chen. Chengkuan Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | A multimodal generative AI copilot for human pathologybreakdown → | 156 |
| 3 | Artificial intelligence for multimodal data integration in oncologybreakdown → | 333 |
| 4 | 55 | |
| 5 | Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningbreakdown → | 261 |
About Chengkuan Chen
Chengkuan Chen is a scholar working on Health Informatics, Biophysics and Artificial Intelligence, having authored 5 papers that have together received 807 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Image Retrieval and Classification Techniques (1 paper). The work is most often cited by research in Health Informatics (120 citations), Radiology, Nuclear Medicine and Imaging (372 citations) and Artificial Intelligence (488 citations). Chengkuan Chen has collaborated with scholars based in United States, Canada and South Korea. Frequent co-authors include Faisal Mahmood, Tiffany Chen, Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson, Andrew D. Trister, Rahul G. Krishnan, Yicong Li, Bowen Chen and Luoting Zhuang. Their work appears in journals such as Nature, Cancer Cell and Nature Biomedical Engineering.
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.