Shilei Cao

553 total citations
13 papers, 298 citations indexed

About

Shilei Cao is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shilei Cao has authored 13 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shilei Cao's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Topic Modeling (4 papers) and Machine Learning in Healthcare (3 papers). Shilei Cao is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Topic Modeling (4 papers) and Machine Learning in Healthcare (3 papers). Shilei Cao collaborates with scholars based in China, United States and Hong Kong. Shilei Cao's co-authors include Yefeng Zheng, Kai Ma, Buyue Qian, Ian Davidson, Jishang Wei, Xianli Zhang, Dong Wei, Xijing Yan, Yinan Deng and Wenqi Shi and has published in prestigious journals such as IEEE Transactions on Medical Imaging, IEEE Transactions on Knowledge and Data Engineering and Medical Image Analysis.

In The Last Decade

Shilei Cao

13 papers receiving 291 citations

Peers

Shilei Cao

RNMI

AI

HEPAT

CVPR

HIM

Grzegorz Chlebus Germany

Soumya Surath Panda India

P.S. Zoumpoulis Greece

Tianyu Shi China

Alexandra Nikita Greece

Lisa Di Jorio Canada

Mehrad Aria Iran

Deepti Mittal India

Hamzeh Asgharnezhad Australia

Grzegorz Chlebus Germany

Shilei Cao

216 ×1.4

RNMI

120 ×0.8

AI

54 ×0.7

HEPAT

111 ×2.3

CVPR

14 ×0.3

HIM

Citations per year, relative to Shilei Cao Shilei Cao (= 1×) peers Grzegorz Chlebus

Countries citing papers authored by Shilei Cao

Since Specialization

Citations

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

Fields of papers citing papers by Shilei Cao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shilei Cao

This figure shows the co-authorship network connecting the top 25 collaborators of Shilei Cao. A scholar is included among the top collaborators of Shilei Cao 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 Shilei Cao. Shilei Cao 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:

13 of 13 papers shown

1.

Qian, Buyue, Jishang Wei, Changchang Yin, et al.. (2023). Adaptive batch mode active learning with deep similarity. Egyptian Informatics Journal. 24(4). 100412–100412. 2 indexed citations

2.

Xie, Xiangjin, Yuxin Chen, Xianli Zhang, et al.. (2022). MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation. ArXiv.org. 2 indexed citations

3.

Zhang, Xianli, et al.. (2021). Context-Aware and Time-Aware Attention-Based Model for Disease Risk Prediction With Interpretability. IEEE Transactions on Knowledge and Data Engineering. 35(4). 3551–3562. 8 indexed citations

4.

Xie, Cong, Shilei Cao, Dong Wei, et al.. (2021). Recist-Net: Lesion Detection Via Grouping Keypoints On Recist-Based Annotation. 921–924. 7 indexed citations

5.

Wang, Shuxin, Shilei Cao, Dong Wei, et al.. (2021). Alternative Baselines for Low-Shot 3D Medical Image Segmentation---An Atlas Perspective. Proceedings of the AAAI Conference on Artificial Intelligence. 35(1). 634–642. 3 indexed citations

6.

Jiang, Yiquan, Shilei Cao, Guoying Wang, et al.. (2020). Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning. Journal of Cancer Research and Clinical Oncology. 147(3). 821–833. 117 indexed citations

7.

Wang, Shuxin, Shilei Cao, Dong Wei, et al.. (2020). Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network. IEEE Transactions on Medical Imaging. 39(12). 4174–4185. 13 indexed citations

8.

Cao, Shilei, et al.. (2020). Pairwise learning for medical image segmentation. Medical Image Analysis. 67. 101876–101876. 20 indexed citations

9.

Zhang, Xianli, et al.. (2020). INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare. 450–460. 30 indexed citations

10.

Li, Yuexiang, Dong Wei, Jiawei Chen, et al.. (2020). Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank. IEEE Journal of Biomedical and Health Informatics. 24(10). 2787–2797. 48 indexed citations

11.

Yin, Changchang, Buyue Qian, Shilei Cao, et al.. (2017). Deep Similarity-Based Batch Mode Active Learning with Exploration-Exploitation. 575–584. 27 indexed citations

12.

Cao, Shilei, Buyue Qian, Changchang Yin, et al.. (2017). Knowledge Guided Short-Text Classification for Healthcare Applications. 31–40. 5 indexed citations

13.

Cao, Shilei, et al.. (2016). Low-Rank Sparse Feature Selection for Patient Similarity Learning. 1335–1340. 16 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.

Explore authors with similar magnitude of impact