Bin Song

817 total citations
44 papers, 543 citations indexed

About

Bin Song is a scholar working on Radiology, Nuclear Medicine and Imaging, Endocrinology, Diabetes and Metabolism and Epidemiology. According to data from OpenAlex, Bin Song has authored 44 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Endocrinology, Diabetes and Metabolism and 13 papers in Epidemiology. Recurrent topics in Bin Song's work include Radiomics and Machine Learning in Medical Imaging (24 papers), Thyroid Cancer Diagnosis and Treatment (16 papers) and Acute Ischemic Stroke Management (9 papers). Bin Song is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (24 papers), Thyroid Cancer Diagnosis and Treatment (16 papers) and Acute Ischemic Stroke Management (9 papers). Bin Song collaborates with scholars based in China, United States and Netherlands. Bin Song's co-authors include Hao Wang, Edwin J.R. van Beek, Matthijs Oudkerk, Paul D. Stein, Yaqiong Ge, Pu‐Yeh Wu, Xilin Sun, Weiyan Liu, Ran Wei and Jing Zhao and has published in prestigious journals such as Medicine, World Journal of Gastroenterology and BioMed Research International.

In The Last Decade

Bin Song

40 papers receiving 532 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bin Song China 12 287 128 126 119 97 44 543
Patrick J. Navin United States 11 108 0.4× 158 1.2× 41 0.3× 100 0.8× 66 0.7× 48 472
Abdullah Alabousi Canada 14 258 0.9× 43 0.3× 49 0.4× 80 0.7× 279 2.9× 49 750
Cristian Giuseppe Monaco Italy 10 123 0.4× 87 0.7× 185 1.5× 15 0.1× 59 0.6× 12 536
Serena Carriero Italy 11 181 0.6× 48 0.4× 36 0.3× 9 0.1× 98 1.0× 51 392
José-Miguel Yamal United States 13 70 0.2× 168 1.3× 14 0.1× 49 0.4× 78 0.8× 22 421
Dan-Mircea Olinic Romania 12 111 0.4× 59 0.5× 71 0.6× 86 0.7× 177 1.8× 69 591
João R. Inácio Canada 15 183 0.6× 79 0.6× 13 0.1× 33 0.3× 213 2.2× 46 571
Rocco Corso Italy 14 208 0.7× 76 0.6× 10 0.1× 25 0.2× 134 1.4× 36 493
Mauro Chiarito Italy 19 127 0.4× 198 1.5× 41 0.3× 93 0.8× 163 1.7× 90 972
Jeanne B. Ackman United States 17 401 1.4× 131 1.0× 54 0.4× 5 0.0× 232 2.4× 50 875

Countries citing papers authored by Bin Song

Since Specialization
Citations

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

Fields of papers citing papers by Bin Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bin Song

This figure shows the co-authorship network connecting the top 25 collaborators of Bin Song. A scholar is included among the top collaborators of Bin Song 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 Bin Song. Bin Song 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.
Zhang, Yuan, et al.. (2024). Predicting Functional Outcomes of Endovascular Thrombectomy in Acute Ischemic Stroke Using a Clinical-Radiomics Nomogram. World Neurosurgery. 193. 911–919. 1 indexed citations
2.
Xia, Tianyi, Tianying Zheng, Shan Huang, et al.. (2024). Fully automated hybrid approach on conventional MRI for triaging clinically significant liver fibrosis: A multi‐center cohort study. Journal of Medical Virology. 96(8). e29882–e29882. 2 indexed citations
3.
Wei, Ran, et al.. (2024). Quantitative Analysis of White Matter Hyperintensities as a Predictor of 1-Year Risk for Ischemic Stroke Recurrence. Neurology and Therapy. 13(5). 1467–1482. 1 indexed citations
4.
Zheng, Tingting, et al.. (2024). Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules. BMC Cancer. 24(1). 256–256. 1 indexed citations
5.
Huang, Jiaqi, He Chang, Peirong Xu, et al.. (2024). Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study. World Journal of Urology. 42(1). 275–275. 3 indexed citations
6.
7.
Hu, Wenjuan, et al.. (2023). MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma. Journal of Multidisciplinary Healthcare. Volume 16. 1–10. 4 indexed citations
8.
9.
Jiang, Hanyu, Yahong Chen, Ting Duan, et al.. (2022). Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT. Annals of Surgical Oncology. 29(7). 4552–4564. 10 indexed citations
10.
Ding, Yi, Hao Wang, Weiyan Liu, et al.. (2022). A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors. Frontiers in Oncology. 12. 677803–677803. 3 indexed citations
11.
Wei, Ran, et al.. (2022). Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma. BMC Medical Imaging. 22(1). 188–188. 3 indexed citations
12.
Yu, Suyun, et al.. (2021). Correlation between TNF‐α ‐308 and +489 Gene Polymorphism and Acute Exacerbation of Chronic Obstructive Pulmonary Diseases. BioMed Research International. 2021(1). 6661281–6661281. 9 indexed citations
13.
Wei, Ran, Wenjuan Hu, Xilin Sun, et al.. (2021). Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer. BMC Medical Imaging. 21(1). 20–20. 20 indexed citations
14.
Hu, Wenjuan, Ran Wei, Shaofeng Duan, et al.. (2020). MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma. Gland Surgery. 9(5). 1214–1226. 30 indexed citations
15.
16.
Wang, Hao, Bin Song, Ningrong Ye, et al.. (2019). Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma. European Journal of Radiology. 122. 108755–108755. 53 indexed citations
17.
Song, Bin, Hao Wang, Yongqi Chen, et al.. (2018). Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma. Medicine. 97(26). e11279–e11279. 8 indexed citations
18.
Song, Bin. (2008). Comparison between the therapeutic effects of cyclophosphamide and tripterygium wilfordii hook on children′s refractory nephrotic syndrome. China Practical Medicine. 2 indexed citations
19.
Chen, Weixia, et al.. (2004). Single-level dynamic spiral CT of hepatocellular carcinoma: Correlation between imaging features and density of tumor microvessels. World Journal of Gastroenterology. 10(1). 67–67. 24 indexed citations
20.
Beek, Edwin J.R. van, et al.. (2001). Clinical Validity of a Normal Pulmonary Angiogram in Patients with Suspected Pulmonary Embolism—A Critical Review. Clinical Radiology. 56(10). 838–842. 124 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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