Guangtai Ding

468 total citations
17 papers, 354 citations indexed

About

Guangtai Ding is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Guangtai Ding has authored 17 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Artificial Intelligence. Recurrent topics in Guangtai Ding's work include Radiomics and Machine Learning in Medical Imaging (3 papers), High-Temperature Coating Behaviors (3 papers) and Additive Manufacturing Materials and Processes (3 papers). Guangtai Ding is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (3 papers), High-Temperature Coating Behaviors (3 papers) and Additive Manufacturing Materials and Processes (3 papers). Guangtai Ding collaborates with scholars based in China, United Kingdom and Japan. Guangtai Ding's co-authors include Huiran Zhang, Dongbo Dai, Xiao Wei, Xu Yan, Jincang Zhang, Tao Xu, Xiaoqiang Li, Haitao Wang, Peilin Chen and Yike Guo and has published in prestigious journals such as Frontiers in Immunology, Journal of Alloys and Compounds and Medicine.

In The Last Decade

Guangtai Ding

15 papers receiving 343 citations

Peers

Guangtai Ding
Yifan Wang China
Hongya Lu United States
Zhuoyuan Li China
Xuejian Li China
Yifeng Lu Germany
Boyuan Ma China
Yang Huang China
Ri Liu China
Yifan Wang China
Guangtai Ding
Citations per year, relative to Guangtai Ding Guangtai Ding (= 1×) peers Yifan Wang

Countries citing papers authored by Guangtai Ding

Since Specialization
Citations

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

Fields of papers citing papers by Guangtai Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guangtai Ding

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

All Works

17 of 17 papers shown
1.
Zhang, Huiran, Rui Hu, Xi Liu, et al.. (2023). An end-to-end machine learning framework exploring phase formation for high entropy alloys. Transactions of Nonferrous Metals Society of China. 33(7). 2110–2120. 9 indexed citations
2.
Cheng, Guoping, He Zhang, Shiting Chen, et al.. (2022). Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer. Frontiers in Immunology. 13. 893198–893198. 39 indexed citations
3.
Wang, Xiangyun, et al.. (2021). Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer. Medicine. 100(20). e25994–e25994. 13 indexed citations
4.
Zhang, Wu, et al.. (2020). NDDR-LCS: A Multi-Task Learning Method for Classification of Carotid Plaques. 2461–2465. 4 indexed citations
5.
Li, Xiaoqiang, et al.. (2020). Spatial-Temporal Knowledge Integration: Robust Self-Supervised Facial Landmark Tracking. 4135–4143. 7 indexed citations
6.
Li, Xiaoqiang, Miao Xie, Yin Zhang, Guangtai Ding, & Weiqin Tong. (2020). Dual attention convolutional network for action recognition. IET Image Processing. 14(6). 1059–1065. 12 indexed citations
7.
Dai, Dongbo, Qing Liu, Xiao Wei, et al.. (2020). Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications. Materials & Design. 196. 109194–109194. 37 indexed citations
8.
Dai, Dongbo, Tao Xu, Xiao Wei, et al.. (2020). Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys. Computational Materials Science. 175. 109618–109618. 154 indexed citations
9.
Li, Xiaoqiang, et al.. (2019). Scale specified single shot multibox detector. IET Computer Vision. 14(2). 59–64. 5 indexed citations
10.
Wang, Lu, et al.. (2018). Data Augmentation with Improved Generative Adversarial Networks. 73–78. 19 indexed citations
11.
Ding, Guangtai, et al.. (2018). L-FCN: A lightweight fully convolutional network for biomedical semantic segmentation. 2363–2367. 14 indexed citations
12.
Zhang, Huiran, et al.. (2018). Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning. Journal of Alloys and Compounds. 782. 110–118. 32 indexed citations
13.
Ding, Guangtai, et al.. (2018). A Novel Automatic Image Stitching Algorithm for Ceramic Microscopic Images. 6 indexed citations
14.
Zhang, Huiran, Qing Li, Zhenjie Feng, et al.. (2015). Automated method for varying the order in which parameters are refined in powder diffraction. Computational Materials Science. 107. 210–215. 2 indexed citations
15.
Wu, Xingfu, Guangtai Ding, & Valerie Taylor. (2014). Parallel Optical Flow Processing of 4D Cardiac CT Data on Multicore Clusters. 113–120. 1 indexed citations
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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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