Chengkuan Chen

1.6k total citations · 3 hit papers
5 papers, 807 citations indexed

About

Chengkuan Chen is a scholar working on Artificial Intelligence, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Chengkuan Chen has authored 5 papers receiving a total of 807 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Chengkuan Chen's 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). Chengkuan Chen is often cited by papers focused on AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Image Retrieval and Classification Techniques (1 paper). Chengkuan Chen collaborates with scholars based in United States, Canada and South Korea. Chengkuan Chen's 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 and has published in prestigious journals such as Nature, Cancer Cell and Nature Biomedical Engineering.

In The Last Decade

Chengkuan Chen

5 papers receiving 794 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Artificial intelligence for multimodal data integration in oncology

2022 333 citations Jana Lipková, Richard J. Chen et al. Cancer Cell profile →
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

2022 261 citations Richard J. Chen, Chengkuan Chen et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
A multimodal generative AI copilot for human pathology

2024 156 citations Ming Y. Lu, Bowen Chen et al. Nature profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chengkuan Chen United States	4	488	372	177	120	113	5	807
Anurag Vaidya United States	8	480 1.0×	407 1.1×	117 0.7×	130 1.1×	130 1.2×	10	865
Guillaume Jaume United States	11	577 1.2×	401 1.1×	210 1.2×	96 0.8×	124 1.1×	14	885
Mane Williams United States	5	431 0.9×	338 0.9×	110 0.6×	87 0.7×	127 1.1×	6	707
Lily H. Peng United States	6	446 0.9×	376 1.0×	96 0.5×	129 1.1×	77 0.7×	6	689
Nikolas Stathonikos Netherlands	14	508 1.0×	328 0.9×	163 0.9×	59 0.5×	78 0.7×	32	687
Andrew Zhang United States	4	362 0.7×	265 0.7×	120 0.7×	78 0.7×	88 0.8×	6	619
Pooya Mobadersany United States	5	432 0.9×	420 1.1×	94 0.5×	58 0.5×	114 1.0×	9	716
Norman Zerbe Germany	12	501 1.0×	314 0.8×	194 1.1×	103 0.9×	68 0.6×	34	780
Safoora Yousefi United States	5	480 1.0×	457 1.2×	97 0.5×	59 0.5×	174 1.5×	7	883
Maha Shady United States	5	354 0.7×	270 0.7×	91 0.5×	57 0.5×	97 0.9×	10	522

Countries citing papers authored by Chengkuan Chen

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

5 of 5 papers shown

1.

Iqbal, Sumaiya, Wei Jiang, Eric R. Hansen, et al.. (2025). Evaluation of DNA encoded library and machine learning model combinations for hit discovery. 2(1). 2 indexed citations

2.

Lu, Ming Y., Bowen Chen, Drew F. K. Williamson, et al.. (2024). A multimodal generative AI copilot for human pathology. Nature. 634(8033). 466–473. 156 indexed citations breakdown →

3.

Lipková, Jana, Richard J. Chen, Bowen Chen, et al.. (2022). Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 40(10). 1095–1110. 333 indexed citations breakdown →

4.

Chen, Chengkuan, et al.. (2022). Fast and scalable search of whole-slide images via self-supervised deep learning. Nature Biomedical Engineering. 6(12). 1420–1434. 55 indexed citations

5.

Chen, Richard J., Chengkuan Chen, Yicong Li, et al.. (2022). Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16123–16134. 261 indexed citations breakdown →

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.

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