Enming Cui

1.0k total citations
34 papers, 656 citations indexed

About

Enming Cui is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Hepatology. According to data from OpenAlex, Enming Cui has authored 34 papers receiving a total of 656 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Pulmonary and Respiratory Medicine and 8 papers in Hepatology. Recurrent topics in Enming Cui's work include Radiomics and Machine Learning in Medical Imaging (20 papers), MRI in cancer diagnosis (8 papers) and Renal cell carcinoma treatment (8 papers). Enming Cui is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), MRI in cancer diagnosis (8 papers) and Renal cell carcinoma treatment (8 papers). Enming Cui collaborates with scholars based in China, United States and Canada. Enming Cui's co-authors include Fan Lin, Wansheng Long, Lei Yi, Kumaresan Sandrasegaran, Ronggang Li, Zhuangsheng Liu, Bao Feng, Yehang Chen, Liangping Luo and Xiangmeng Chen and has published in prestigious journals such as Nature Communications, ACS Nano and ACS Applied Materials & Interfaces.

In The Last Decade

Enming Cui

33 papers receiving 650 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Enming Cui China 14 461 333 134 125 104 34 656
Yaqiong Ge China 15 477 1.0× 169 0.5× 121 0.9× 115 0.9× 113 1.1× 47 615
Guangjie Yang China 14 389 0.8× 241 0.7× 100 0.7× 113 0.9× 102 1.0× 47 575
Alexander Baur Germany 18 327 0.7× 412 1.2× 159 1.2× 59 0.5× 91 0.9× 52 805
Siya Shi China 8 357 0.8× 175 0.5× 184 1.4× 70 0.6× 137 1.3× 13 515
Edward W. Johnston United Kingdom 17 360 0.8× 394 1.2× 89 0.7× 74 0.6× 76 0.7× 55 719
Xingyu Zhao China 14 519 1.1× 251 0.8× 70 0.5× 108 0.9× 69 0.7× 31 670
Brandon A. Dyer United States 14 301 0.7× 161 0.5× 93 0.7× 151 1.2× 146 1.4× 45 626
Xueyi Zheng China 9 418 0.9× 110 0.3× 77 0.6× 101 0.8× 102 1.0× 15 605
Maria Chiara Brunese Italy 15 357 0.8× 133 0.4× 189 1.4× 134 1.1× 183 1.8× 60 601
Yini Huang China 10 493 1.1× 107 0.3× 75 0.6× 131 1.0× 67 0.6× 23 665

Countries citing papers authored by Enming Cui

Since Specialization
Citations

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

Fields of papers citing papers by Enming Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Enming Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Enming Cui. A scholar is included among the top collaborators of Enming Cui 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 Enming Cui. Enming Cui 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.
Tang, Chun’an, Enming Cui, J. Zhou, et al.. (2025). Triple-Scale Structure-Induced Efficient Passive Radiative Cooling Combining Robust Anticondensation. ACS Nano. 19(20). 19384–19393. 3 indexed citations
2.
Chen, Yehang, Yuan Chen, Peijun Li, et al.. (2025). General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis. Nature Communications. 16(1). 2097–2097. 5 indexed citations
3.
Feng, Bao, et al.. (2024). Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model. Abdominal Radiology. 49(5). 1397–1410. 7 indexed citations
4.
Feng, Bao, Yu Liu, Qinghui Hu, et al.. (2024). Deep learning vs. robust federal learning for distinguishing adrenal metastases from benign lesions with multi-phase CT images. Heliyon. 10(3). e25655–e25655. 3 indexed citations
5.
Feng, Bao, Zhiqi Yang, Shi‐Ting Feng, et al.. (2024). Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence. Nature Communications. 15(1). 742–742. 25 indexed citations
6.
Bai, Xiangge, Enming Cui, Xu Wang, et al.. (2024). Multibioinspired Hybrid Superwetting Surface for Efficient Fog Collection and Power Generation. ACS Applied Materials & Interfaces. 16(33). 44298–44304. 4 indexed citations
7.
Feng, Bao, et al.. (2024). Predicting postoperative prognosis in clear cell renal cell carcinoma using a multiphase CT-based deep learning model. Abdominal Radiology. 50(5). 2152–2159. 1 indexed citations
8.
Feng, Bao, Xiangmeng Chen, Yehang Chen, et al.. (2023). Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning. Cancers. 15(3). 892–892. 8 indexed citations
9.
Lin, Fan, et al.. (2023). Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists. European Journal of Radiology. 169. 111169–111169. 2 indexed citations
10.
11.
Tu, Ning, Feng Sun, Zhi Wen, et al.. (2021). Detecting Muscle Invasion of Bladder Cancer Using a Proposed Magnetic Resonance Imaging Strategy. Journal of Magnetic Resonance Imaging. 54(4). 1212–1221. 19 indexed citations
12.
Cui, Enming, et al.. (2021). Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. Abdominal Radiology. 46(8). 3866–3876. 16 indexed citations
13.
Cui, Enming, Qing Li, Yingjie Mei, et al.. (2020). Combination of hepatocyte fraction and diffusion-weighted imaging as a predictor in quantitative hepatic fibrosis evaluation. Abdominal Radiology. 45(11). 3681–3689. 3 indexed citations
14.
Lin, Fan, Jinpeng Xu, Lei Yi, et al.. (2020). A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma. European Journal of Radiology. 129. 109079–109079. 34 indexed citations
15.
16.
Wang, Qiushi, et al.. (2019). Distribution and correlation of pancreatic gland size and duct diameters on MRCP in patients without evidence of pancreatic disease. Abdominal Radiology. 44(3). 967–975. 12 indexed citations
17.
Deng, Yu, Erik Soule, Enming Cui, et al.. (2019). CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. European Radiology. 29(12). 6922–6929. 59 indexed citations
18.
Deng, Youjun, Erik Soule, Enming Cui, et al.. (2019). Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clinical Radiology. 75(2). 108–115. 28 indexed citations
19.
Lin, Fan, Enming Cui, Lei Yi, & Liangping Luo. (2019). CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdominal Radiology. 44(7). 2528–2534. 53 indexed citations
20.
Sandrasegaran, Kumar, et al.. (2018). Can functional parameters from hepatobiliary phase of gadoxetate MRI predict clinical outcomes in patients with cirrhosis?. European Radiology. 28(10). 4215–4224. 13 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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