Juebin Jin

565 total citations
25 papers, 387 citations indexed

About

Juebin Jin is a scholar working on Radiology, Nuclear Medicine and Imaging, Obstetrics and Gynecology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Juebin Jin has authored 25 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Obstetrics and Gynecology and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Juebin Jin's work include Radiomics and Machine Learning in Medical Imaging (19 papers), MRI in cancer diagnosis (8 papers) and Endometrial and Cervical Cancer Treatments (6 papers). Juebin Jin is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), MRI in cancer diagnosis (8 papers) and Endometrial and Cervical Cancer Treatments (6 papers). Juebin Jin collaborates with scholars based in China, Switzerland and South Korea. Juebin Jin's co-authors include Xiance Jin, Congying Xie, Yao Ai, Ji Zhang, Haiyan Zhu, Ce Han, Xiaomin Zheng, Didi Chen, Cong Liu and Xia Deng and has published in prestigious journals such as Radiotherapy and Oncology, European Radiology and Frontiers in Oncology.

In The Last Decade

Juebin Jin

25 papers receiving 381 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juebin Jin China 12 307 116 72 64 60 25 387
Yao Ai China 13 306 1.0× 119 1.0× 47 0.7× 92 1.4× 44 0.7× 28 378
Nicolette Taku United States 11 166 0.5× 103 0.9× 81 1.1× 42 0.7× 60 1.0× 23 353
C. Balleyguier France 9 134 0.4× 212 1.8× 40 0.6× 59 0.9× 37 0.6× 16 340
Chang Suk Park South Korea 10 331 1.1× 44 0.4× 92 1.3× 34 0.5× 50 0.8× 25 532
Xingyu Zhao China 14 519 1.7× 251 2.2× 70 1.0× 31 0.5× 69 1.1× 31 670
Michele Telegrafo Italy 14 422 1.4× 101 0.9× 86 1.2× 22 0.3× 68 1.1× 45 663
Jieun Koh South Korea 13 189 0.6× 68 0.6× 89 1.2× 17 0.3× 64 1.1× 25 394
Minghao Li China 12 92 0.3× 102 0.9× 75 1.0× 64 1.0× 80 1.3× 40 353
Yangyang Kan China 6 326 1.1× 60 0.5× 24 0.3× 99 1.5× 40 0.7× 6 375
Manon Beuque Netherlands 5 396 1.3× 136 1.2× 47 0.7× 13 0.2× 74 1.2× 11 469

Countries citing papers authored by Juebin Jin

Since Specialization
Citations

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

Fields of papers citing papers by Juebin Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juebin Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Juebin Jin. A scholar is included among the top collaborators of Juebin Jin 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 Juebin Jin. Juebin Jin 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.
Liu, Yapeng, Yangyang Zhang, Wenlong Li, et al.. (2024). Radiomics Harmonization in Ultrasound Images for Cervical Cancer Lymph Node Metastasis Prediction Using Cycle-GAN. Technology in Cancer Research & Treatment. 23. 2234044125–2234044125. 1 indexed citations
2.
Ai, Yao, Xiaoyang Zhu, Wenlong Li, et al.. (2024). MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer. Radiotherapy and Oncology. 197. 110328–110328. 1 indexed citations
3.
Xie, Congying, Chenyu Li, Li Shao, et al.. (2024). Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy. Radiotherapy and Oncology. 199. 110438–110438. 11 indexed citations
4.
Cao, Z. Alexander, Zhenhua Zhang, Juebin Jin, et al.. (2023). Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics. Insights into Imaging. 14(1). 174–174. 13 indexed citations
5.
Yu, Wenliang, et al.. (2023). Direct Dose Prediction With Deep Learning for Postoperative Cervical Cancer Underwent Volumetric Modulated Arc Therapy. Technology in Cancer Research & Treatment. 22. 2223908927–2223908927. 11 indexed citations
6.
Zhang, Zhenhua, Ji Zhang, Yao Ai, et al.. (2023). Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer. Insights into Imaging. 14(1). 65–65. 19 indexed citations
7.
Zhu, Hui, Yanyan Li, Yuhua Zhang, et al.. (2023). Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study. PeerJ. 11. e14546–e14546. 8 indexed citations
8.
Zhang, Lei, Qiao Zheng, Juebin Jin, et al.. (2022). The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technology in Cancer Research & Treatment. 21. 2213860300–2213860300. 5 indexed citations
9.
10.
Li, Yanyan, Yao Ai, Juebin Jin, et al.. (2022). Differentiate Thyroid Follicular Adenoma from Carcinoma with Combined Ultrasound Radiomics Features and Clinical Ultrasound Features. Journal of Digital Imaging. 35(5). 1362–1372. 19 indexed citations
11.
Zhu, Haiyan, Yao Ai, Ji Zhang, et al.. (2021). Preoperative Nomogram for Differentiation of Histological Subtypes in Ovarian Cancer Based on Computer Tomography Radiomics. Frontiers in Oncology. 11. 642892–642892. 13 indexed citations
12.
13.
Ai, Yao, et al.. (2021). Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors. Frontiers in Oncology. 11. 610742–610742. 23 indexed citations
14.
Mercier, François, Lin An, Benjamin Wu, et al.. (2021). Assessing the impact of organ-specific lesion dynamics on survival in patients with recurrent urothelial carcinoma treated with atezolizumab or chemotherapy. ESMO Open. 7(1). 100346–100346. 9 indexed citations
15.
Jin, Juebin, Haiyan Zhu, Yao Ai, et al.. (2021). Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer. Frontiers in Oncology. 10. 614201–614201. 45 indexed citations
16.
17.
Shen, Lanxiao, Juebin Jin, Ce Han, et al.. (2019). <p>Association of lung and heart dose with survival in patients with non-small cell lung cancer underwent volumetric modulated arc therapy</p>. Cancer Management and Research. Volume 11. 6091–6098. 6 indexed citations
18.
Jin, Xiance, Xiaomin Zheng, Didi Chen, et al.. (2019). Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. European Radiology. 29(11). 6080–6088. 64 indexed citations
19.
Sun, Yuan, et al.. (2015). Rumex acetosa L. induces vasorelaxation in rat aorta via activation of PI3-kinase/Akt- AND Ca(2+)-eNOS-NO signaling in endothelial cells.. PubMed. 66(6). 907–15. 9 indexed citations
20.
Jin, Juebin, et al.. (2002). Simulation and Modeling of a Micro Pressure Sensor Array. TechConnect Briefs. 1(2002). 306–309. 1 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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