Hui Cui

1.7k total citations
97 papers, 1.1k citations indexed

About

Hui Cui is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Hui Cui has authored 97 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Radiology, Nuclear Medicine and Imaging, 28 papers in Computer Vision and Pattern Recognition and 27 papers in Artificial Intelligence. Recurrent topics in Hui Cui's work include Radiomics and Machine Learning in Medical Imaging (32 papers), Computational Drug Discovery Methods (16 papers) and AI in cancer detection (13 papers). Hui Cui is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (32 papers), Computational Drug Discovery Methods (16 papers) and AI in cancer detection (13 papers). Hui Cui collaborates with scholars based in China, Australia and Japan. Hui Cui's co-authors include Ping Xuan, Tiangang Zhang, Xiuying Wang, Qiangguo Jin, Changming Sun, Ran Su, Zhaopeng Meng, Dagan Feng, Toshiya Nakaguchi and Nan Sheng and has published in prestigious journals such as Journal of Clinical Oncology, Bioinformatics and Scientific Reports.

In The Last Decade

Hui Cui

88 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hui Cui China 18 356 313 255 248 152 97 1.1k
Xiuquan Du China 18 153 0.4× 245 0.8× 502 2.0× 165 0.7× 158 1.0× 54 1.1k
Qingqi Hong China 13 255 0.7× 191 0.6× 164 0.6× 276 1.1× 29 0.2× 48 819
Hong Zhao China 22 779 2.2× 79 0.3× 180 0.7× 592 2.4× 375 2.5× 106 1.7k
Ovidiu Daescu United States 16 214 0.6× 149 0.5× 111 0.4× 170 0.7× 51 0.3× 82 839
Alioune Ngom Canada 18 255 0.7× 67 0.2× 540 2.1× 147 0.6× 122 0.8× 90 1.2k
Zizhao Zhang United States 19 824 2.3× 421 1.3× 90 0.4× 789 3.2× 39 0.3× 42 1.6k
Luis Rueda Canada 19 289 0.8× 75 0.2× 509 2.0× 106 0.4× 108 0.7× 122 1.1k
João Sanches Portugal 24 278 0.8× 568 1.8× 265 1.0× 455 1.8× 19 0.1× 111 1.9k
Yiqun Hu China 23 199 0.6× 57 0.2× 280 1.1× 938 3.8× 56 0.4× 110 1.8k

Countries citing papers authored by Hui Cui

Since Specialization
Citations

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

Fields of papers citing papers by Hui Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hui Cui. 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 Hui Cui. The network helps show where Hui Cui may publish in the future.

Co-authorship network of co-authors of Hui Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Hui Cui. A scholar is included among the top collaborators of Hui Cui 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 Hui Cui. Hui Cui 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.
Xuan, Ping, Rui Wang, Jing Gu, Hui Cui, & Tiangang Zhang. (2025). Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes. BMC Bioinformatics. 26(1). 231–231.
2.
Chen, Dongliang, Tiangang Zhang, Hui Cui, Jing Gu, & Ping Xuan. (2025). KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction. Journal of Chemical Information and Modeling. 65(9). 4714–4728. 1 indexed citations
3.
Xuan, Ping, et al.. (2025). Multi-Knowledge Graph and Multi-View Entity Feature Learning for Predicting Drug-Related Side Effects. Journal of Chemical Information and Modeling. 65(10). 5124–5138. 2 indexed citations
4.
Cui, Hui, Qiangguo Jin, Xixi Wu, et al.. (2024). Evolving graph convolutional network with transformer for CT segmentation. Applied Soft Computing. 165. 112069–112069. 3 indexed citations
5.
Cui, Hui, et al.. (2024). Exploring object reduction approaches for optimizing decision-making in linguistic concept formal context. Applied Intelligence. 54(19). 9088–9104.
6.
Cui, Hui, et al.. (2024). Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis Using Histopathological Images. IEEE Transactions on Medical Imaging. 43(8). 2888–2900. 6 indexed citations
7.
8.
Xuan, Ping, et al.. (2024). Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images. Computers in Biology and Medicine. 177. 108640–108640.
9.
Xuan, Ping, Jing Gu, Hui Cui, et al.. (2024). Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes. Bioinformatics. 40(2). 8 indexed citations
10.
Xuan, Ping, et al.. (2024). Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction. Computers in Biology and Medicine. 184. 109321–109321. 2 indexed citations
11.
Xuan, Ping, et al.. (2024). Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction. IEEE Journal of Biomedical and Health Informatics. 28(7). 4306–4316. 1 indexed citations
12.
Xuan, Ping, et al.. (2024). Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction. Journal of Chemical Information and Modeling. 64(16). 6662–6675. 1 indexed citations
13.
Qu, Yanpeng, et al.. (2023). Stratified multi-density spectral clustering using Gaussian mixture model. Information Sciences. 633. 182–203. 7 indexed citations
14.
15.
Xuan, Ping, Xixi Wu, Hui Cui, et al.. (2022). Multi-scale random walk driven adaptive graph neural network with dual-head neighboring node attention for CT segmentation. Applied Soft Computing. 133. 109905–109905. 6 indexed citations
16.
Xuan, Ping, et al.. (2022). Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(2). 1480–1491. 6 indexed citations
17.
Xuan, Ping, Hui Cui, Tiangang Zhang, et al.. (2021). Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes. Knowledge-Based Systems. 236. 107360–107360. 25 indexed citations
18.
Song, Yingying, et al.. (2021). Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(5). 2963–2974. 3 indexed citations
19.
Chen, Zhuangzhi, Hui Cui, Liang Huang, et al.. (2021). SigNet: A Novel Deep Learning Framework for Radio Signal Classification. IEEE Transactions on Cognitive Communications and Networking. 8(2). 529–541. 70 indexed citations
20.
Wang, Xiuying, et al.. (2017). A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data. IEEE Transactions on Fuzzy Systems. 26(3). 1671–1687. 56 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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