Si‐Min Ruan

968 total citations
43 papers, 657 citations indexed

About

Si‐Min Ruan is a scholar working on Hepatology, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Si‐Min Ruan has authored 43 papers receiving a total of 657 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Hepatology, 19 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Oncology. Recurrent topics in Si‐Min Ruan's work include Hepatocellular Carcinoma Treatment and Prognosis (19 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and MRI in cancer diagnosis (10 papers). Si‐Min Ruan is often cited by papers focused on Hepatocellular Carcinoma Treatment and Prognosis (19 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and MRI in cancer diagnosis (10 papers). Si‐Min Ruan collaborates with scholars based in China, Spain and France. Si‐Min Ruan's co-authors include Ming‐De Lu, Wei Wang, Xiaoyan Xie, Ming Kuang, Li‐Da Chen, Xiao-wen Huang, Yang Huang, Zhu Wang, Shuling Chen and Jinyu Liang and has published in prestigious journals such as Radiology, IEEE Transactions on Image Processing and Life Sciences.

In The Last Decade

Si‐Min Ruan

40 papers receiving 649 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Si‐Min Ruan China 14 351 320 171 103 91 43 657
Yongyan Gao China 13 295 0.8× 255 0.8× 162 0.9× 155 1.5× 238 2.6× 18 795
Kensaku Mori Japan 14 328 0.9× 227 0.7× 179 1.0× 221 2.1× 134 1.5× 63 763
Hong Ai China 9 279 0.8× 191 0.6× 208 1.2× 73 0.7× 73 0.8× 17 620
Marco Calandri Italy 16 375 1.1× 290 0.9× 126 0.7× 191 1.9× 246 2.7× 52 892
Erjiao Xu China 13 219 0.6× 339 1.1× 166 1.0× 193 1.9× 141 1.5× 48 589
Kim‐Nhien Vu Canada 8 195 0.6× 271 0.8× 223 1.3× 95 0.9× 69 0.8× 16 498
Yongping Lu China 6 254 0.7× 143 0.4× 143 0.8× 49 0.5× 66 0.7× 9 440
Diana Feier Romania 17 319 0.9× 461 1.4× 508 3.0× 132 1.3× 135 1.5× 49 1.0k
Sheela Agarwal United States 13 318 0.9× 156 0.5× 110 0.6× 115 1.1× 76 0.8× 25 667

Countries citing papers authored by Si‐Min Ruan

Since Specialization
Citations

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

Fields of papers citing papers by Si‐Min Ruan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Si‐Min Ruan. 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 Si‐Min Ruan. The network helps show where Si‐Min Ruan may publish in the future.

Co-authorship network of co-authors of Si‐Min Ruan

This figure shows the co-authorship network connecting the top 25 collaborators of Si‐Min Ruan. A scholar is included among the top collaborators of Si‐Min Ruan 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 Si‐Min Ruan. Si‐Min Ruan 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.
2.
Li, Ming‐De, Si‐Min Ruan, Huaping Zhang, et al.. (2025). Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging. Frontiers in Robotics and AI. 12. 1527686–1527686. 4 indexed citations
3.
Li, Ming‐De, Wei Li, Manxia Lin, et al.. (2024). Systematic comparison of deep-learning based fusion strategies for multi-modal ultrasound in diagnosis of liver cancer. Neurocomputing. 603. 128257–128257. 8 indexed citations
4.
Li, Ming‐De, Jianchao Zhang, Si‐Min Ruan, et al.. (2024). Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound. BMC Medical Imaging. 24(1). 1 indexed citations
5.
Chen, Li‐Da, Hong Yang, Ming‐De Li, et al.. (2024). US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening. Radiology. 311(1). e231461–e231461. 5 indexed citations
6.
Huang, Hui, Si‐Min Ruan, Ping Xu, et al.. (2023). Complementary Role of CEUS and CT/MR LI-RADS for Diagnosis of Recurrent HCC. Cancers. 15(24). 5743–5743. 8 indexed citations
7.
Li, Ming‐De, Si‐Min Ruan, Li‐Da Chen, et al.. (2023). ADMNet: Adaptive-Weighting Dual Mapping for Online Tracking With Respiratory Motion Estimation in Contrast-Enhanced Ultrasound. IEEE Transactions on Image Processing. 33. 58–68. 4 indexed citations
8.
Tong, Wenjuan, Shaohong Wu, Hui Huang, et al.. (2023). Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management. JAMA Network Open. 6(5). e2313674–e2313674. 29 indexed citations
9.
Tian, Xiaoyu, Si‐Min Ruan, Ming‐De Li, et al.. (2023). A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma. PubMed. 28. 1–4. 1 indexed citations
10.
Huang, Hui, Chaoqun Li, Danni He, et al.. (2023). Surveillance for malignant progression of LI-RADS version 2017 category 3/4 nodules using contrast-enhanced ultrasound. European Radiology. 33(12). 9336–9346.
11.
Ruan, Si‐Min, Hui Huang, Manxia Lin, et al.. (2022). Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions. La radiologia medica. 128(1). 6–15. 13 indexed citations
12.
Wang, Wei, Li‐Da Chen, Si‐Min Ruan, et al.. (2021). Artificial intelligence assists identifying malignant versus benign liver lesions using contrast‐enhanced ultrasound. Journal of Gastroenterology and Hepatology. 36(10). 2875–2883. 46 indexed citations
13.
Lü, Xiaozhou, Si‐Min Ruan, Xin Zheng, et al.. (2021). Contrast‐Enhanced Ultrasound‐Based Nomogram. Journal of Ultrasound in Medicine. 41(8). 1925–1938. 3 indexed citations
14.
Li, Wei, Bowen Zhuang, Si‐Min Ruan, et al.. (2021). Inter-reader agreement of CEUS LI-RADS among radiologists with different levels of experience. European Radiology. 31(9). 6758–6767. 17 indexed citations
15.
Huang, Yang, Wei Li, Si‐Min Ruan, et al.. (2021). Contrast-enhanced US diagnostic algorithm of hepatocellular carcinoma in patients with occult hepatitis B. Abdominal Radiology. 47(2). 608–617. 6 indexed citations
16.
Zhang, Jianchao, Zhu Wang, Si‐Min Ruan, et al.. (2019). Assessment of angiogenesis in rabbit orthotropic liver tumors using three-dimensional dynamic contrast-enhanced ultrasound compared with two-dimensional DCE-US. Japanese Journal of Radiology. 37(10). 701–709. 3 indexed citations
17.
18.
Chen, Li‐Da, Si‐Min Ruan, Yuan Lin, et al.. (2018). Comparison between M-score and LR-M in the reporting system of contrast-enhanced ultrasound LI-RADS. European Radiology. 29(8). 4249–4257. 36 indexed citations
19.
Li, Wei, Yang Huang, Guangjian Liu, et al.. (2018). Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis. European Radiology. 29(3). 1496–1506. 94 indexed citations
20.
Lai, ZhiCheng, Jinyu Liang, Li‐Da Chen, et al.. (2018). Do hepatocellular carcinomas located in subcapsular space or in proximity to vessels increase the rate of local tumor progression? A meta-analysis. Life Sciences. 207. 381–385. 17 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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