Jinke Wang

1.1k total citations
53 papers, 728 citations indexed

About

Jinke Wang is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Jinke Wang has authored 53 papers receiving a total of 728 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 23 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Artificial Intelligence. Recurrent topics in Jinke Wang's work include Medical Image Segmentation Techniques (18 papers), Advanced Neural Network Applications (11 papers) and Retinal Imaging and Analysis (9 papers). Jinke Wang is often cited by papers focused on Medical Image Segmentation Techniques (18 papers), Advanced Neural Network Applications (11 papers) and Retinal Imaging and Analysis (9 papers). Jinke Wang collaborates with scholars based in China, Japan and United States. Jinke Wang's co-authors include Changfa Shi, Haiying Wang, Shinichi Tamura, Yuanzhi Cheng, Yadong Wang, Dawei Zhang, Li Peng, Dengju Yao, Kensaku Mori and Xiaorong Zhan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Advanced Functional Materials and Scientific Reports.

In The Last Decade

Jinke Wang

47 papers receiving 708 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jinke Wang China 14 240 239 181 110 74 53 728
Yongyi Lu China 14 186 0.8× 403 1.7× 233 1.3× 123 1.1× 81 1.1× 37 1.0k
Zhen Chen China 17 193 0.8× 317 1.3× 307 1.7× 86 0.8× 49 0.7× 85 860
Dahong Qian China 21 664 2.8× 259 1.1× 221 1.2× 265 2.4× 156 2.1× 61 1.3k
Jiawei Li China 14 175 0.7× 145 0.6× 325 1.8× 138 1.3× 66 0.9× 51 775
Qiaoliang Li China 14 599 2.5× 448 1.9× 99 0.5× 132 1.2× 62 0.8× 49 985
Andrea Bianchi Brazil 14 120 0.5× 265 1.1× 360 2.0× 65 0.6× 33 0.4× 72 720
Xianli Zhang China 17 115 0.5× 148 0.6× 356 2.0× 64 0.6× 198 2.7× 58 953
Yanbei Liu China 14 143 0.6× 216 0.9× 203 1.1× 36 0.3× 32 0.4× 54 648
Xinglong Liu China 16 491 2.0× 162 0.7× 266 1.5× 110 1.0× 56 0.8× 36 964
Yu Qiao China 15 113 0.5× 233 1.0× 134 0.7× 62 0.6× 112 1.5× 56 827

Countries citing papers authored by Jinke Wang

Since Specialization
Citations

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

Fields of papers citing papers by Jinke Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinke Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Jinke Wang. A scholar is included among the top collaborators of Jinke Wang 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 Jinke Wang. Jinke Wang 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.
Jiang, Jingang, et al.. (2025). Segmentation algorithms of dental CT images: A comprehensive review from classical to deep learning trend. Expert Systems with Applications. 275. 126853–126853.
2.
Wang, Jinke, et al.. (2025). A novel omni-dimensional dynamic convolution-based feature fusion framework for liver and tumor segmentation from CT. Biomedical Signal Processing and Control. 112. 108572–108572.
3.
4.
Wang, Xiaoxue, Jinke Wang, Rongqiao Wang, et al.. (2024). Assessing the corrosion protection property of coatings loaded with corrosion inhibitors using the real-time atmospheric corrosion monitoring technique. International Journal of Minerals Metallurgy and Materials. 32(1). 119–126. 6 indexed citations
5.
6.
Wang, Jinke, et al.. (2024). CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images. Medical & Biological Engineering & Computing. 63(4). 1027–1044.
7.
Wang, Jinke, et al.. (2023). Few-shot node classification on attributed networks based on deep metric learning for Cyber–Physical–Social Services. Pattern Recognition Letters. 173. 87–92. 5 indexed citations
8.
Wang, Fei, Ting Zhang, Jinke Wang, et al.. (2022). Alterations in Plasma Lipidomic Profiles in Adult Patients with Schizophrenia and Major Depressive Disorder. Medicina. 58(11). 1509–1509. 9 indexed citations
9.
Wang, Jinke, et al.. (2022). Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT. Journal of Digital Imaging. 35(6). 1479–1493. 15 indexed citations
10.
Wang, Jinke, et al.. (2022). Deep supervision and atrous inception-based U-Net combining CRF for automatic liver segmentation from CT. Scientific Reports. 12(1). 16995–16995. 11 indexed citations
11.
Zhang, Chunchun, et al.. (2021). Federated Tensor Decomposition-Based Feature Extraction Approach for Industrial IoT. IEEE Transactions on Industrial Informatics. 17(12). 8541–8549. 22 indexed citations
12.
Ma, Lingwei, Jinke Wang, Juantao Zhang, et al.. (2021). Graphene oxide–cerium oxide hybrids for enhancement of mechanical properties and corrosion resistance of epoxy coatings. Journal of Materials Science. 56(16). 10108–10123. 59 indexed citations
13.
Wang, Jinke, et al.. (2021). SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver CT segmentation.. arXiv (Cornell University). 1 indexed citations
14.
Wang, Jinke, et al.. (2021). SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography. Computer Methods and Programs in Biomedicine. 208. 106268–106268. 107 indexed citations
15.
Wang, Jinke, et al.. (2021). An improved residual U-Net with morphological-based loss function for automatic liver segmentation in computed tomography. Mathematical Biosciences & Engineering. 19(2). 1426–1447. 8 indexed citations
16.
Xu, Xinhui, Tao Luo, Jinliang Gao, et al.. (2020). CRISPR-Assisted DNA Detection: A Novel dCas9-Based DNA Detection Technique. The CRISPR Journal. 3(6). 487–502. 27 indexed citations
17.
Wang, Jinke, et al.. (2020). A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis. Medical & Biological Engineering & Computing. 58(10). 2413–2425. 5 indexed citations
18.
Wang, Jinke & Changfa Shi. (2017). Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy. BioMedical Engineering OnLine. 16(1). 49–49. 18 indexed citations
19.
Wang, Jinke, et al.. (2017). An Improved Variable Quoit Filter Based Liver Tumor Detection in CT Combining Support Vector Machine Classification. Journal of Medical Imaging and Health Informatics. 7(5). 982–988. 1 indexed citations
20.
Cheng, Yuanzhi, et al.. (2014). Automatic centerline detection of small three-dimensional vessel structures. Journal of Electronic Imaging. 23(1). 13007–13007. 7 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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