Meihao Wang

1.3k total citations
51 papers, 868 citations indexed

About

Meihao Wang is a scholar working on Radiology, Nuclear Medicine and Imaging, Psychiatry and Mental health and Cognitive Neuroscience. According to data from OpenAlex, Meihao Wang has authored 51 papers receiving a total of 868 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Psychiatry and Mental health and 10 papers in Cognitive Neuroscience. Recurrent topics in Meihao Wang's work include Radiomics and Machine Learning in Medical Imaging (23 papers), MRI in cancer diagnosis (13 papers) and Attention Deficit Hyperactivity Disorder (11 papers). Meihao Wang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (23 papers), MRI in cancer diagnosis (13 papers) and Attention Deficit Hyperactivity Disorder (11 papers). Meihao Wang collaborates with scholars based in China, United States and Taiwan. Meihao Wang's co-authors include Jiejie Zhou, Min‐Ying Su, Yang Zhang, Yezhi Lin, Jiance Li, Zhifang Pan, Peter Chang, Daniel Chow, Ouchen Wang and Rita S. Mehta and has published in prestigious journals such as Journal of Affective Disorders, American Journal of Roentgenology and Hippocampus.

In The Last Decade

Meihao Wang

46 papers receiving 858 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Meihao Wang China 15 570 296 162 79 78 51 868
Shih-Ying Huang United States 9 439 0.8× 185 0.6× 172 1.1× 67 0.8× 38 0.5× 15 776
Jiejie Zhou China 12 394 0.7× 246 0.8× 85 0.5× 26 0.3× 41 0.5× 31 562
Yu‐Chuan Hu China 18 779 1.4× 124 0.4× 131 0.8× 162 2.1× 111 1.4× 32 1.2k
Ulf Neuberger Germany 10 492 0.9× 67 0.2× 142 0.9× 82 1.0× 52 0.7× 15 761
Hai‐Yan Nan China 13 468 0.8× 92 0.3× 118 0.7× 50 0.6× 71 0.9× 26 837
Dapeng Shi China 16 536 0.9× 124 0.4× 153 0.9× 30 0.4× 20 0.3× 32 766
David Clunie United States 19 351 0.6× 244 0.8× 209 1.3× 85 1.1× 16 0.2× 44 838
Xiaoer Wei China 17 414 0.7× 107 0.4× 216 1.3× 264 3.3× 37 0.5× 70 1.1k
Hidetaka Arimura Japan 19 819 1.4× 245 0.8× 547 3.4× 113 1.4× 33 0.4× 123 1.3k
Alfiia Galimzianova United States 8 438 0.8× 263 0.9× 107 0.7× 35 0.4× 39 0.5× 15 1.0k

Countries citing papers authored by Meihao Wang

Since Specialization
Citations

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

Fields of papers citing papers by Meihao Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meihao Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Meihao Wang. A scholar is included among the top collaborators of Meihao 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 Meihao Wang. Meihao 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
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Chen, Shuangli, Ronghui Zhou, Xueqin Bai, et al.. (2025). Interaction between DRD4 and MAOA genetic polymorphisms on static and dynamic regional coherence in the prefrontal cortex of drug-naive children with ADHD. Journal of Affective Disorders. 390. 120014–120014.
4.
Zhou, Ronghui, Lei Xie, Jingqiang Wang, et al.. (2024). Characterization of local white matter microstructural alterations in Alzheimer’s disease: A reproducible study. Computers in Biology and Medicine. 179. 108750–108750. 2 indexed citations
5.
Zhu, Ying, Yaru Wei, Zhongwei Chen, et al.. (2024). Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study. Insights into Imaging. 15(1). 211–211. 2 indexed citations
6.
Zhou, Jiejie, Huiru Liu, Shuxin Ye, et al.. (2024). Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience. European Radiology. 35(1). 140–150. 4 indexed citations
7.
Bai, Xueqin, Tao Guo, Xiaojun Guan, et al.. (2024). The association of motor reserve and clinical progression in Parkinson’s disease. NeuroImage Clinical. 44. 103704–103704. 1 indexed citations
8.
Luan, Xiaoqian, et al.. (2024). A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer’s Disease. Translational Psychiatry. 14(1). 111–111. 3 indexed citations
9.
Zhang, Yang, et al.. (2024). Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT. Academic Radiology. 32(3). 1287–1296. 4 indexed citations
10.
Wei, Yaru, Haojie Wang, Zhongwei Chen, et al.. (2023). Deep Learning‐Based Multiparametric MRI Model for Preoperative T‐Stage in Rectal Cancer. Journal of Magnetic Resonance Imaging. 59(3). 1083–1092. 15 indexed citations
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Su, Yanping, Chenying Lu, Lin Shen, et al.. (2023). Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram. Frontiers in Oncology. 13. 1006172–1006172. 5 indexed citations
13.
Lou, Lihua, Meihao Wang, Xiang Zhang, et al.. (2022). Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process. Computational Intelligence and Neuroscience. 2022. 1–14. 9 indexed citations
14.
Chen, Xiaohong, Qingshan Deng, Qiang Wang, et al.. (2022). Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks. Frontiers in Public Health. 10. 891766–891766. 8 indexed citations
15.
Chen, Xiaohong, Yang Zhang, Jiahuan Zhou, et al.. (2022). Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning. Frontiers in Oncology. 12. 991892–991892. 8 indexed citations
16.
Hu, Zhishan, Zijing Wu, Shuang Yang, et al.. (2022). Higher Stress Hyperglycemia Ratio Is Associated With a Higher Risk of Stroke-Associated Pneumonia. Frontiers in Nutrition. 9. 784114–784114. 25 indexed citations
17.
Chen, Shuangli, Ronghui Zhou, Chuang Yang, et al.. (2021). Different effects of the DRD4 genotype on intrinsic brain network connectivity strength in drug-naïve children with ADHD and healthy controls. Brain Imaging and Behavior. 16(1). 464–475. 10 indexed citations
18.
Zhou, Ronghui, Shuangli Chen, Chuang Yang, et al.. (2021). Altered Variability and Concordance of Dynamic Resting-State fMRI Indices in Patients With Attention Deficit Hyperactivity Disorder. Frontiers in Neuroscience. 15. 731596–731596. 7 indexed citations
19.
Zhou, Jiejie, Shuxin Ye, Huiru Liu, et al.. (2020). Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI. Clinical Breast Cancer. 21(5). 440–449.e1. 22 indexed citations
20.
Zhou, Ming, Chuang Yang, Xuan Bu, et al.. (2019). Abnormal functional network centrality in drug-naïve boys with attention-deficit/hyperactivity disorder. European Child & Adolescent Psychiatry. 28(10). 1321–1328. 23 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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