Dayang Wang

489 total citations · 1 hit paper
19 papers, 255 citations indexed

About

Dayang Wang is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dayang Wang has authored 19 papers receiving a total of 255 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Biomedical Engineering, 7 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dayang Wang's work include Medical Imaging Techniques and Applications (7 papers), Advanced X-ray and CT Imaging (7 papers) and Advanced Chemical Sensor Technologies (3 papers). Dayang Wang is often cited by papers focused on Medical Imaging Techniques and Applications (7 papers), Advanced X-ray and CT Imaging (7 papers) and Advanced Chemical Sensor Technologies (3 papers). Dayang Wang collaborates with scholars based in United States, China and Spain. Dayang Wang's co-authors include Hengyong Yu, Fenglei Fan, Zhan Wu, Rui Liu, Fei Wang, Boce Zhang, Yaguang Luo, Zhen Jia, Arne J. Pearlstein and Tingting Gu and has published in prestigious journals such as IEEE Access, IEEE Transactions on Medical Imaging and Biosensors and Bioelectronics.

In The Last Decade

Dayang Wang

15 papers receiving 248 citations

Hit Papers

align trajectories

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.

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising

2023 111 citations Dayang Wang, Fenglei Fan et al. Physics in Medicine and Biology profile →

Peers

Dayang Wang

BE

RNMI

CVPR

MB

AI

Pingli Ma China

Fei Ma China

Yu‐Jung Tsai United States

G. Gopakumar India

Khamael Al-Dulaimi Australia

Hongcheng Fan China

M.V. Ranganath United States

Yuanhong Yu China

Seonghyeon Nam South Korea

Yuhang Dong United States

Pingli Ma China

Dayang Wang

34 ×0.2

BE

44 ×0.4

RNMI

68 ×0.8

CVPR

34 ×0.8

MB

71 ×3.2

AI

Citations per year, relative to Dayang Wang Dayang Wang (= 1×) peers Pingli Ma

Countries citing papers authored by Dayang Wang

Since Specialization

Citations

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

Fields of papers citing papers by Dayang Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dayang Wang

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

19 of 19 papers shown

1.

Ji, Yongcheng, et al.. (2025). Strength prediction of recycled concrete using hybrid artificial intelligence models with Gaussian noise addition. Engineering Applications of Artificial Intelligence. 149. 110566–110566. 7 indexed citations

2.

Li, Mengzhou, et al.. (2025). Patch-based dual-domain photon-counting CT data correction with residual-based WGAN-ViT. Physics in Medicine and Biology. 70(4). 45008–45008.

3.

Jia, Zhen, Yaguang Luo, Dayang Wang, et al.. (2024). Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays. Biosensors and Bioelectronics. 248. 115999–115999. 18 indexed citations

4.

Wang, Dayang, et al.. (2024). Task Offloading in Real-Time Distributed Energy Power Systems. Electronics. 13(14). 2747–2747.

5.

Zhou, L. P., et al.. (2024). Gradient Guided Co-Retention Feature Pyramid Network for LDCT Image Denoising. Lecture notes in computer science. 15012. 153–163. 2 indexed citations

6.

Wang, Dayang, Fenglei Fan, Bojian Hou, et al.. (2024). Manifoldron: Direct Space Partition via Manifold Discovery. IEEE Transactions on Neural Networks and Learning Systems. 36(7). 12311–12325.

7.

Wang, Dayang, et al.. (2024). LoMAE: Simple Streamlined Low-Level Masked Autoencoders for Robust, Generalized, and Interpretable Low-Dose CT Denoising. IEEE Journal of Biomedical and Health Informatics. 28(11). 6815–6827. 2 indexed citations

8.

Wang, Dayang, et al.. (2024). Physics-Informed Score-Based Diffusion Model for Limited-Angle Reconstruction of Cardiac Computed Tomography. IEEE Transactions on Medical Imaging. 44(9). 3629–3640. 4 indexed citations

9.

Wang, Dayang, et al.. (2024). Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography. Physics in Medicine and Biology. 69(10). 105012–105012. 3 indexed citations

10.

Wu, Zhan, Dayang Wang, Ruifeng Chen, et al.. (2023). Deep Dual-Domain United Guiding Learning With Global–Local Transformer-Convolution U-Net for LDCT Reconstruction. IEEE Transactions on Instrumentation and Measurement. 72. 1–15. 14 indexed citations

11.

Wang, Dayang, et al.. (2023). Masked Autoencoders for Low-dose CT Denoising. 1–4. 10 indexed citations

12.

Wang, Dayang, et al.. (2023). Sq-Swin: Siamese Quadratic Swin Transformer for Lettuce Browning Prediction. SSRN Electronic Journal. 1 indexed citations

13.

Wang, Dayang, Fenglei Fan, Zhan Wu, et al.. (2023). CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising. Physics in Medicine and Biology. 68(6). 65012–65012. 111 indexed citations breakdown →

14.

Wang, Dayang, et al.. (2023). SQ-Swin: Siamese Quadratic Swin Transformer for Lettuce Browning Prediction. IEEE Access. 11. 128724–128735. 3 indexed citations

15.

Luo, Yaguang, Zhen Jia, Dayang Wang, et al.. (2022). Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network. Food Research International. 162(Pt B). 112052–112052. 18 indexed citations

16.

Fan, Fenglei, Dayang Wang, Qikui Zhu, et al.. (2021). On a Sparse Shortcut Topology of Artificial Neural Networks. IEEE Transactions on Artificial Intelligence. 3(4). 595–608. 10 indexed citations

17.

Jia, Zhen, Yaguang Luo, Dayang Wang, et al.. (2021). Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network. Biosensors and Bioelectronics. 183. 113209–113209. 39 indexed citations

18.

Li, Wei, et al.. (2020). Improved Security Schemes for LTE Power Wireless Private Communication System. 20. 103–106. 1 indexed citations

19.

Liu, Yudong, Dayang Wang, Yingyu Ren, & Ningde Jin. (2019). Detecting the Flow Pattern Transition in the Gas-Liquid Two-Phase Flow Using Multivariate Multi-Scale Entropy Analysis. Zeitschrift für Naturforschung A. 74(10). 837–848. 12 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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