E Linning

531 total citations
21 papers, 377 citations indexed

About

E Linning is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, E Linning has authored 21 papers receiving a total of 377 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Pulmonary and Respiratory Medicine, 11 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Biomedical Engineering. Recurrent topics in E Linning's work include Lung Cancer Diagnosis and Treatment (12 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (8 papers). E Linning is often cited by papers focused on Lung Cancer Diagnosis and Treatment (12 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (8 papers). E Linning collaborates with scholars based in China, United States and Czechia. E Linning's co-authors include Binsheng Zhao, Lin Lü, Lawrence H. Schwartz, Li Li, Hao Yang, Hao Yang, Daqing Ma, Yan Xu, Zhifeng Wu and Zhenghan Yang and has published in prestigious journals such as Expert Systems with Applications, American Journal of Roentgenology and Medical Physics.

In The Last Decade

E Linning

21 papers receiving 369 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
E Linning China 12 306 238 90 48 38 21 377
Fangyuan Qu China 6 351 1.1× 281 1.2× 60 0.7× 68 1.4× 30 0.8× 7 437
Jeong Hoon Kim South Korea 4 310 1.0× 116 0.5× 66 0.7× 53 1.1× 27 0.7× 7 339
Dominik Deniffel Germany 11 228 0.7× 169 0.7× 113 1.3× 65 1.4× 22 0.6× 25 359
Çağrı Erdim Türkiye 7 308 1.0× 258 1.1× 102 1.1× 28 0.6× 30 0.8× 17 366
Ahmad Algohary United States 8 367 1.2× 274 1.2× 84 0.9× 40 0.8× 75 2.0× 16 440
M.V. Villas Spain 5 436 1.4× 179 0.8× 197 2.2× 82 1.7× 68 1.8× 6 503
Shonket Ray United States 7 278 0.9× 201 0.8× 93 1.0× 37 0.8× 138 3.6× 14 373
Hoi Yin Loi Singapore 8 354 1.2× 148 0.6× 102 1.1× 96 2.0× 42 1.1× 20 426
Yangkang Jiang China 10 255 0.8× 109 0.5× 153 1.7× 24 0.5× 21 0.6× 15 319

Countries citing papers authored by E Linning

Since Specialization
Citations

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

Fields of papers citing papers by E Linning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of E Linning

This figure shows the co-authorship network connecting the top 25 collaborators of E Linning. A scholar is included among the top collaborators of E Linning 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 E Linning. E Linning 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.
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Wang, Xu, et al.. (2025). Assessing pulmonary vascular remodeling in connective tissue disease-associated interstitial lung disease: a CT-based quantitative analysis. Quantitative Imaging in Medicine and Surgery. 15(4). 3333–3346. 1 indexed citations
3.
Liu, Xueyu, et al.. (2024). Transformer based multiple superpixel-instance learning for weakly supervised segmenting lesions of interstitial lung disease. Expert Systems with Applications. 253. 124270–124270. 4 indexed citations
4.
Liu, Xueyu, et al.. (2024). Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images. Journal of X-Ray Science and Technology. 32(2). 323–338. 2 indexed citations
5.
6.
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Hou, Fan Fan, et al.. (2023). Computed Tomography–Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease–Associated Interstitial Lung Disease. Journal of Computer Assisted Tomography. 47(5). 738–745. 5 indexed citations
8.
Hou, Fan Fan, et al.. (2022). Quantitative assessment of interstitial lung disease based on RDNet convolutional network. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1550–1553. 1 indexed citations
9.
Xu, Yan, Lin Lü, Shawn Sun, et al.. (2021). Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes. European Radiology. 32(3). 1517–1527. 23 indexed citations
10.
Linning, E, et al.. (2021). Recognition of honeycomb lung in CT images based on improved MobileNet model. Medical Physics. 48(8). 4304–4315. 19 indexed citations
11.
Lü, Lin, Shawn Sun, Hao Yang, et al.. (2020). Radiomics Prediction of EGFR Status in Lung Cancer—Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data. Tomography. 6(2). 223–230. 27 indexed citations
12.
Lü, Lin, Deling Wang, Lili Wang, et al.. (2020). A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma. European Radiology. 30(7). 3614–3623. 12 indexed citations
13.
Zhang, Na, et al.. (2020). Can Peritumoral Radiomics Improve the Prediction of Malignancy of Solid Pulmonary Nodule Smaller Than 2 cm?. Academic Radiology. 29. S47–S52. 15 indexed citations
14.
Linning, E, Yan Xu, Zhifeng Wu, et al.. (2020). Differentiation of Focal-Type Autoimmune Pancreatitis From Pancreatic Ductal Adenocarcinoma Using Radiomics Based on Multiphasic Computed Tomography. Journal of Computer Assisted Tomography. 44(4). 511–518. 19 indexed citations
15.
Wang, Kai, et al.. (2019). Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis. Frontiers in Oncology. 9. 1229–1229. 26 indexed citations
16.
Linning, E, Lin Lü, Li Li, et al.. (2019). Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography. Journal of Computer Assisted Tomography. 43(2). 300–306. 52 indexed citations
17.
Xu, Yan, Lin Lü, E Linning, et al.. (2019). Application of Radiomics in Predicting the Malignancy of Pulmonary Nodules in Different Sizes. American Journal of Roentgenology. 213(6). 1213–1220. 39 indexed citations
18.
Linning, E, Lin Lü, Li Li, et al.. (2018). Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. Academic Radiology. 26(9). 1245–1252. 67 indexed citations
19.
Linning, E, et al.. (2013). Computed tomography quantitative analysis of components: a new method monitoring the growth of pulmonary nodule. Acta Radiologica. 54(8). 904–908. 6 indexed citations
20.
Linning, E & Daqing Ma. (2009). Volumetric Measurement Pulmonary Ground-Glass Opacity Nodules with Multi-detector CT. Academic Radiology. 16(8). 934–939. 30 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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