Rongguo Zhang

2.2k total citations
68 papers, 1.3k citations indexed

About

Rongguo Zhang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Rongguo Zhang has authored 68 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Computer Vision and Pattern Recognition and 13 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Rongguo Zhang's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (8 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Rongguo Zhang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (8 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Rongguo Zhang collaborates with scholars based in China, United States and United Kingdom. Rongguo Zhang's co-authors include Haibo Xu, Lan Lan, Dan Xu, Minhua Yu, Jing Hu, Zhibo Chen, Meng Yang, Kang Han, Pei Liang and Xiaodong Shen and has published in prestigious journals such as Remote Sensing of Environment, Stroke and Chemical Engineering Journal.

In The Last Decade

Rongguo Zhang

60 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rongguo Zhang China 20 399 215 194 170 161 68 1.3k
Zhen Zhou China 19 338 0.8× 213 1.0× 34 0.2× 164 1.0× 63 0.4× 107 1.2k
Chirag Ahuja India 20 276 0.7× 165 0.8× 81 0.4× 201 1.2× 401 2.5× 237 2.1k
Cong Gao China 22 131 0.3× 102 0.5× 38 0.2× 305 1.8× 130 0.8× 70 1.6k
Giuseppe La Tona Italy 17 191 0.5× 89 0.4× 268 1.4× 63 0.4× 72 0.4× 69 846
Kazuhiro Shimizu Japan 29 225 0.6× 342 1.6× 482 2.5× 269 1.6× 133 0.8× 225 3.2k
Moussa Mansour United States 37 634 1.6× 113 0.5× 169 0.9× 323 1.9× 92 0.6× 165 6.1k
Francisco Caramelo Portugal 25 228 0.6× 194 0.9× 25 0.1× 174 1.0× 52 0.3× 147 1.8k
Takayuki Tsuji Japan 26 137 0.3× 254 1.2× 39 0.2× 89 0.5× 54 0.3× 124 2.1k
Huai Chen China 17 288 0.7× 138 0.6× 105 0.5× 102 0.6× 24 0.1× 86 890

Countries citing papers authored by Rongguo Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Rongguo Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rongguo Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Rongguo Zhang. A scholar is included among the top collaborators of Rongguo Zhang 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 Rongguo Zhang. Rongguo Zhang 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.
Wang, Hongxiao, et al.. (2025). Optimal Transport and Central Moment Consistency Regularization for Semi-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging. 44(8). 3397–3409.
2.
3.
Cao, Junhui, Jing Hu, & Rongguo Zhang. (2024). Adaptive graph learning algorithm for incomplete multi-view clustered image segmentation. Engineering Applications of Artificial Intelligence. 139. 109264–109264.
4.
Liu, Min, Anqi Liu, Kang Han, et al.. (2024). Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases. Journal of Imaging Informatics in Medicine. 37(1). 268–279. 3 indexed citations
5.
Zhang, Rongguo, et al.. (2024). Construction and Validation of a General Medical Image Dataset for Pretraining. Journal of Imaging Informatics in Medicine. 38(2). 1051–1061.
6.
7.
Liu, Anqi, et al.. (2023). Development and validation of a lung graph–based machine learning model to predict acute pulmonary thromboembolism on chest noncontrast computed tomography. Quantitative Imaging in Medicine and Surgery. 13(10). 6710–6723. 3 indexed citations
8.
Liu, Min, Han Sung Kang, Peiyao Zhang, et al.. (2022). Quantitative analysis of high-resolution computed tomography features of idiopathic pulmonary fibrosis: a structure-function correlation study. Quantitative Imaging in Medicine and Surgery. 12(7). 3655–3665. 9 indexed citations
9.
Tang, Lei, Chuanren Liu, Rongguo Zhang, Zongtao Duan, & Yunji Liang. (2021). Who Will Travel With Me? Personalized Ranking Using Attributed Network Embedding for Pooling. IEEE Transactions on Intelligent Transportation Systems. 23(8). 12311–12327. 2 indexed citations
10.
Zhang, Hongxia, et al.. (2021). Clot burden of acute pulmonary thromboembolism: comparison of two deep learning algorithms, Qanadli score, and Mastora score. Quantitative Imaging in Medicine and Surgery. 12(1). 66–79. 15 indexed citations
11.
Liu, Zhenguo, Yujie Yuan, Lei Yang, et al.. (2021). 3D DenseNet Deep Learning Based Preoperative Computed Tomography for Detecting Myasthenia Gravis in Patients With Thymoma. Frontiers in Oncology. 11. 631964–631964. 18 indexed citations
12.
Zhou, Qing-Qing, Wen Tang, Jiashuo Wang, et al.. (2021). Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network. Clinical Imaging. 81. 24–32. 19 indexed citations
13.
Liu, Weifang, Xiaojuan Guo, Peiyao Zhang, et al.. (2020). Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning. European Radiology. 30(6). 3567–3575. 74 indexed citations
14.
Liu, Jinsha, Wen Tang, Huiling Zhang, et al.. (2020). Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework. European Journal of Radiology. 129. 109013–109013. 21 indexed citations
15.
Fang, Yijie, Wei Li, Xiaojun Chen, et al.. (2020). Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. European Radiology. 31(4). 1831–1842. 79 indexed citations
16.
Liang, Sen, Rongguo Zhang, Tao Ai, et al.. (2018). Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas. Genes. 9(8). 382–382. 107 indexed citations
17.
Hu, Jing, et al.. (2018). A Multiscale Fusion Convolutional Neural Network for Plant Leaf Recognition. IEEE Signal Processing Letters. 25(6). 853–857. 108 indexed citations
18.
Wang, Shuangkun, Rongguo Zhang, Yufeng Deng, et al.. (2018). Discrimination of smoking status by MRI based on deep learning method. Quantitative Imaging in Medicine and Surgery. 8(11). 1113–1120. 11 indexed citations
19.
Cai, Xinyuan, Baihua Xiao, Chunheng Wang, & Rongguo Zhang. (2011). A local learning based Image-To-Class distance for image classification. 27. 667–671. 1 indexed citations
20.
Wu, Hui, Huiling Guo, Jiaheng Lei, Rongguo Zhang, & Yong Liu. (2007). Research on synthesis and action mechanism of polycarboxylate superplasticizer. Frontiers of Chemistry in China. 2(3). 322–325. 10 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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