Wenbing Lv

1.3k total citations
44 papers, 1.0k citations indexed

About

Wenbing Lv is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Wenbing Lv has authored 44 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Biomedical Engineering and 12 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Wenbing Lv's work include Radiomics and Machine Learning in Medical Imaging (35 papers), Medical Imaging Techniques and Applications (19 papers) and Advanced X-ray and CT Imaging (15 papers). Wenbing Lv is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (35 papers), Medical Imaging Techniques and Applications (19 papers) and Advanced X-ray and CT Imaging (15 papers). Wenbing Lv collaborates with scholars based in China, United States and Canada. Wenbing Lv's co-authors include Lijun Lu, Jianhua Ma, Arman Rahmim, Qianjin Feng, Wufan Chen, Qingyu Yuan, Quanshi Wang, Jun Jiang, Tuanjie Li and Yuming Jiang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Physics in Medicine and Biology.

In The Last Decade

Wenbing Lv

41 papers receiving 998 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wenbing Lv China 15 874 424 250 183 126 44 1.0k
Lianzhen Zhong China 15 711 0.8× 389 0.9× 103 0.4× 208 1.1× 155 1.2× 22 934
Qingyu Yuan China 15 669 0.8× 397 0.9× 132 0.5× 242 1.3× 106 0.8× 23 878
Xinming Zhao China 17 1.0k 1.2× 248 0.6× 163 0.7× 252 1.4× 213 1.7× 51 1.3k
Pengfei Yang China 16 832 1.0× 402 0.9× 220 0.9× 291 1.6× 89 0.7× 38 1.1k
Sonia Skamene Canada 9 718 0.8× 328 0.8× 222 0.9× 127 0.7× 141 1.1× 21 923
Fusheng Ouyang China 13 836 1.0× 281 0.7× 171 0.7× 186 1.0× 125 1.0× 30 1.1k
Shufang Pei China 12 833 1.0× 212 0.5× 137 0.5× 134 0.7× 229 1.8× 23 1.0k
Sarah A. Mattonen Canada 13 796 0.9× 457 1.1× 214 0.9× 138 0.8× 171 1.4× 36 963
Jessica Goya-Outi France 4 864 1.0× 317 0.7× 189 0.8× 180 1.0× 81 0.6× 5 964

Countries citing papers authored by Wenbing Lv

Since Specialization
Citations

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

Fields of papers citing papers by Wenbing Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wenbing Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Wenbing Lv. A scholar is included among the top collaborators of Wenbing Lv 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 Wenbing Lv. Wenbing Lv 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, Haiyan, et al.. (2025). EGNL-FAT: An Edge-Guided Non-Local network with Frequency-Aware transformer for smoke segmentation. Expert Systems with Applications. 280. 127621–127621.
3.
Lv, Wenbing, Chen‐Fei Wu, Zhilong Chen, et al.. (2025). A Serial MRI–based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma. Radiology Artificial Intelligence. 7(2). e230544–e230544.
4.
Lv, Wenbing, et al.. (2025). LMSST-GCN: Longitudinal MRI sub-structural texture guided graph convolution network for improved progression prediction of knee osteoarthritis. Computer Methods and Programs in Biomedicine. 261. 108600–108600. 1 indexed citations
5.
Salimi, Yazdan, Wenbing Lv, Hongwen Chen, et al.. (2024). Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET. EJNMMI Physics. 11(1). 66–66.
6.
Peng, Lihong, et al.. (2024). BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis. Physics in Medicine and Biology. 69(10). 105007–105007. 2 indexed citations
7.
Xu, Hui, Wenbing Lv, Hao Zhang, et al.. (2023). Multimodality radiomics analysis based on [18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis. European Radiology. 33(10). 6677–6688. 6 indexed citations
8.
Liu, Jinghua, Xiaolei Zhang, Zhongxiao Wang, et al.. (2023). Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. EJNMMI Research. 13(1). 6 indexed citations
9.
Wu, Huiqin, Xiaohui Liu, Lihong Peng, et al.. (2023). Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer. Physics in Medicine and Biology. 68(22). 225014–225014. 2 indexed citations
10.
Lv, Wenbing, et al.. (2023). MMS-Net: Multi-level multi-scale feature extraction network for medical image segmentation. Biomedical Signal Processing and Control. 86. 105330–105330. 24 indexed citations
11.
Peng, Lihong, Hui Xu, Wenbing Lv, Lijun Lu, & Wufan Chen. (2023). Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers. 15(3). 932–932. 2 indexed citations
12.
Lv, Wenbing, et al.. (2023). Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging. Computer Methods and Programs in Biomedicine. 230. 107341–107341. 9 indexed citations
13.
14.
Lv, Wenbing, et al.. (2022). Deep learning–based harmonization of CT reconstruction kernels towards improved clinical task performance. European Radiology. 33(4). 2426–2438. 6 indexed citations
15.
Chen, Xiaohong, et al.. (2022). Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma. Frontiers in Oncology. 12. 788968–788968. 17 indexed citations
16.
Shao, Dan, Haiping Liu, Hao Zhang, et al.. (2021). Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model. Frontiers in Oncology. 11. 721318–721318. 10 indexed citations
17.
Lv, Wenbing, et al.. (2021). GapFill-Recon Net: A Cascade Network for simultaneously PET Gap Filling and Image Reconstruction. Computer Methods and Programs in Biomedicine. 208. 106271–106271. 9 indexed citations
18.
Jiang, Yuming, Wei Wang, Chuanli Chen, et al.. (2019). Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer. Frontiers in Oncology. 9. 340–340. 58 indexed citations
19.
Jiang, Yuming, Chuanli Chen, Jingjing Xie, et al.. (2018). Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine. 36. 171–182. 145 indexed citations
20.
Jiang, Yuming, Qingyu Yuan, Wenbing Lv, et al.. (2018). Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics. 8(21). 5915–5928. 103 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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