S. Kevin Zhou

9.2k total citations · 1 hit paper
197 papers, 4.5k citations indexed

About

S. Kevin Zhou is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, S. Kevin Zhou has authored 197 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Computer Vision and Pattern Recognition, 68 papers in Radiology, Nuclear Medicine and Imaging and 49 papers in Artificial Intelligence. Recurrent topics in S. Kevin Zhou's work include Medical Image Segmentation Techniques (35 papers), Radiomics and Machine Learning in Medical Imaging (32 papers) and Advanced Neural Network Applications (22 papers). S. Kevin Zhou is often cited by papers focused on Medical Image Segmentation Techniques (35 papers), Radiomics and Machine Learning in Medical Imaging (32 papers) and Advanced Neural Network Applications (22 papers). S. Kevin Zhou collaborates with scholars based in United States, China and Germany. S. Kevin Zhou's co-authors include Rama Chellappa, Dorin Comaniciu, B. Moghaddam, Hui Ding, Bo Zhou, Volker Krueger, Li Xiao, Jingdan Zhang, Hu Han and Bogdan Georgescu and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

S. Kevin Zhou

174 papers receiving 4.4k citations

Hit Papers

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net ... 2017 2026 2020 2023 2017 50 100 150 200 250
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.

  1. FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
    2017 294 citations S. Kevin Zhou, Rama Chellappa et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Kevin Zhou United States 35 2.6k 1.4k 1.1k 990 289 197 4.5k
Ghassan Hamarneh Canada 39 2.7k 1.0× 1.9k 1.3× 1.8k 1.7× 1.1k 1.1× 129 0.4× 252 6.7k
Benoı̂t Macq Belgium 35 3.0k 1.2× 880 0.6× 661 0.6× 678 0.7× 752 2.6× 298 5.4k
Dimitris Metaxas United States 40 3.3k 1.3× 1.5k 1.0× 1.4k 1.3× 906 0.9× 181 0.6× 181 6.0k
Ioannis A. Kakadiaris United States 41 3.6k 1.4× 985 0.7× 635 0.6× 870 0.9× 833 2.9× 266 6.1k
Baiying Lei China 48 2.6k 1.0× 2.4k 1.7× 3.1k 3.0× 741 0.7× 263 0.9× 296 8.1k
Shaoting Zhang United States 39 3.9k 1.5× 2.1k 1.5× 2.2k 2.1× 958 1.0× 184 0.6× 249 6.8k
Yefeng Zheng China 44 3.1k 1.2× 2.1k 1.5× 2.2k 2.1× 1.2k 1.2× 196 0.7× 293 6.8k
Huazhong Shu China 41 3.3k 1.3× 1.5k 1.1× 549 0.5× 1.2k 1.2× 386 1.3× 296 5.5k
Aly A. Farag United States 30 3.4k 1.3× 1.0k 0.7× 699 0.7× 535 0.5× 121 0.4× 304 5.2k
Bernhard Preim Germany 36 2.3k 0.9× 873 0.6× 475 0.5× 639 0.6× 168 0.6× 341 4.5k

Countries citing papers authored by S. Kevin Zhou

Since Specialization
Citations

This map shows the geographic impact of S. Kevin Zhou'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 S. Kevin Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Kevin Zhou more than expected).

Fields of papers citing papers by S. Kevin Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Kevin Zhou. 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 S. Kevin Zhou. The network helps show where S. Kevin Zhou may publish in the future.

Co-authorship network of co-authors of S. Kevin Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of S. Kevin Zhou. A scholar is included among the top collaborators of S. Kevin Zhou 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 S. Kevin Zhou. S. Kevin Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhou, S. Kevin, et al.. (2026). A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource. IEEE Transactions on Knowledge and Data Engineering. 1–20.
2.
Xie, Xike, et al.. (2025). Capsule: An Out-of-Core Training Mechanism for Colossal GNNs. Proceedings of the ACM on Management of Data. 3(1). 1–30. 2 indexed citations
3.
Jin, Junwei, et al.. (2025). Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition. Interdisciplinary Sciences Computational Life Sciences. 17(1). 215–230. 1 indexed citations
4.
Wang, Yuhao, Xueyuan Zhang, Xiaolong Li, et al.. (2025). Computational pathology in precision oncology: Evolution from task-specific models to foundation models. Chinese Medical Journal. 138(22). 2868–2878. 1 indexed citations
5.
Jiang, Zihang, et al.. (2025). MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation. Medical Image Analysis. 103. 103606–103606. 3 indexed citations
6.
Quan, Quan, et al.. (2025). Deep generalizable prediction of RNA secondary structure via base pair motif energy. Nature Communications. 16(1). 5856–5856.
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Yang, Jingyuan, Wenxuan Liang, Xingde Li, et al.. (2024). O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision. Medical Image Analysis. 99. 103319–103319. 4 indexed citations
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Yao, Qingsong, et al.. (2024). Which images to label for few-shot medical image analysis?. Medical Image Analysis. 96. 103200–103200. 4 indexed citations
13.
Yang, Shuxin, Xian Wu, Shen Ge, et al.. (2023). Radiology report generation with a learned knowledge base and multi-modal alignment. Medical Image Analysis. 86. 102798–102798. 78 indexed citations
14.
Zhou, S. Kevin, et al.. (2022). Effect of Rainy Weather on Transmission Performance of FSO System using BPSK Modulation Scheme. 127–130. 3 indexed citations
15.
Zhang, Wentai, Yanghua Fan, He Wang, et al.. (2021). Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing’s Disease. Frontiers in Endocrinology. 12. 635795–635795. 25 indexed citations
16.
Wei, Xiao-Yong, et al.. (2021). Deep Collocative Learning for Immunofixation Electrophoresis Image Analysis. IEEE Transactions on Medical Imaging. 40(7). 1898–1910. 10 indexed citations
17.
Peng, Cheng, Wei-An Lin, Rama Chellappa, & S. Kevin Zhou. (2020). Towards multi-sequence MR image recovery from undersampled k-space data. 614–623. 1 indexed citations
18.
Zhou, S. Kevin. (2015). Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches. 17 indexed citations
19.
Wang, Peng, et al.. (2010). Graph Based Interactive Detection of Curve Structures in 2D Fluoroscopy. Lecture notes in computer science. 13(Pt 3). 269–277. 8 indexed citations
20.
Zheng, Yefeng, Xiang Zhou, Bogdan Georgescu, S. Kevin Zhou, & Dorin Comaniciu. (2006). Example based non-rigid shape detection. 1 indexed citations

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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