Wenpei Bai

1.5k total citations
91 papers, 1.0k citations indexed

About

Wenpei Bai is a scholar working on Endocrinology, Diabetes and Metabolism, Reproductive Medicine and Obstetrics and Gynecology. According to data from OpenAlex, Wenpei Bai has authored 91 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Endocrinology, Diabetes and Metabolism, 24 papers in Reproductive Medicine and 22 papers in Obstetrics and Gynecology. Recurrent topics in Wenpei Bai's work include Menopause: Health Impacts and Treatments (25 papers), Estrogen and related hormone effects (17 papers) and Gynecological conditions and treatments (14 papers). Wenpei Bai is often cited by papers focused on Menopause: Health Impacts and Treatments (25 papers), Estrogen and related hormone effects (17 papers) and Gynecological conditions and treatments (14 papers). Wenpei Bai collaborates with scholars based in China, United States and Thailand. Wenpei Bai's co-authors include Lihua Qin, Jing Jia, Yu Sun, Jihong Kang, Ke Wang, Xing Chen, Sainan Zhu, Jianli Liu, Wenjuan Wang and Qiyang Shen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Neuroscience.

In The Last Decade

Wenpei Bai

82 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wenpei Bai China 19 331 233 209 202 158 91 1.0k
A. R. Genazzani Italy 22 317 1.0× 311 1.3× 230 1.1× 135 0.7× 169 1.1× 54 1.1k
Lihong Peng China 21 150 0.5× 136 0.6× 212 1.0× 458 2.3× 45 0.3× 32 1.2k
Dariusz Kajdaniuk Poland 26 418 1.3× 146 0.6× 128 0.6× 191 0.9× 29 0.2× 137 1.8k
L. M. Demers United States 22 427 1.3× 334 1.4× 253 1.2× 88 0.4× 53 0.3× 30 1.4k
Chishimba Nathan Mowa United States 18 190 0.6× 123 0.5× 136 0.7× 35 0.2× 67 0.4× 53 867
Yun Shi China 18 228 0.7× 62 0.3× 183 0.9× 103 0.5× 32 0.2× 73 936
Danielle Hiam Australia 20 93 0.3× 348 1.5× 135 0.6× 53 0.3× 81 0.5× 46 1.1k
Roberto Mioni Italy 21 465 1.4× 517 2.2× 114 0.5× 47 0.2× 54 0.3× 54 1.2k
Bo Bjerre Sweden 18 276 0.8× 115 0.5× 182 0.9× 109 0.5× 41 0.3× 43 1.4k
Elizabeth Barrett-Connor United States 12 1.1k 3.3× 152 0.7× 563 2.7× 232 1.1× 33 0.2× 13 2.0k

Countries citing papers authored by Wenpei Bai

Since Specialization
Citations

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

Fields of papers citing papers by Wenpei Bai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wenpei Bai

This figure shows the co-authorship network connecting the top 25 collaborators of Wenpei Bai. A scholar is included among the top collaborators of Wenpei Bai 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 Wenpei Bai. Wenpei Bai 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.
Lyu, Shuchang, Qi Zhao, Wenpei Bai, et al.. (2025). Unsupervised cross-domain semantic segmentation on multi-modality ovarian tumor ultrasound data. Pattern Recognition. 171. 112311–112311.
2.
Wang, Xiaoxue, et al.. (2024). Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning. International Journal of Medical Informatics. 188. 105480–105480. 4 indexed citations
3.
4.
Bai, Wenpei, et al.. (2024). Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations. Scientific Data. 11(1). 410–410. 4 indexed citations
5.
Chen, Lijiang, et al.. (2023). Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images. Frontiers in Oncology. 13. 1154200–1154200. 15 indexed citations
6.
Zhang, Meng, et al.. (2023). An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI. Diagnostics. 13(9). 1525–1525. 9 indexed citations
7.
Bai, Wenpei, et al.. (2023). WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm. Bioengineering. 10(8). 945–945. 1 indexed citations
8.
9.
Wei, Sheng, et al.. (2022). Identification of hypoxia-related prognostic signature for ovarian cancer based on Cox regression model. European Journal of Gynaecological Oncology. 43(2). 247–247. 4 indexed citations
10.
11.
Wang, X., et al.. (2021). Efficacy and safety of autologous platelet‐rich fibrin for the treatment of infertility with intrauterine adhesions. Journal of obstetrics and gynaecology research. 47(11). 3883–3894. 11 indexed citations
12.
Zhao, Jin, Yanbin Zhang, Xueying Yang, et al.. (2021). Risk factors of pleural effusion after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in late-stage and recurrent ovarian cancer. Annals of Palliative Medicine. 10(1). 385–391. 4 indexed citations
13.
Zhao, Jian, Ying Dong, Xia Zhao, et al.. (2020). Photodetection and Safety of 5‐Aminolevulinic Acid‐Induced Porphyrin in Patients With Cervical Intraepithelial Neoplasia. Lasers in Surgery and Medicine. 53(5). 654–663. 9 indexed citations
14.
Bai, Wenpei, et al.. (2018). Cross-sectional study of contraceptive use among Chinese women of reproductive age: results based on a mobile application (APP)-derived data. Archives of Gynecology and Obstetrics. 297(5). 1193–1199. 2 indexed citations
15.
Chen, Rui, Bilgin Keserci, Xiaoying Wang, et al.. (2016). The safety and effectiveness of volumetric magnetic resonance-guided high-intensity focused ultrasound treatment of symptomatic uterine fibroids: early clinical experience in China. Journal of Therapeutic Ultrasound. 4(1). 27–27. 22 indexed citations
16.
Chen, Yuke, Wei Yu, Yang� Yang, et al.. (2015). Association between overactive bladder and peri-menopause syndrome: a cross-sectional study of female physicians in China. International Urology and Nephrology. 47(5). 743–749. 16 indexed citations
17.
Wang, Wenjuan, Wenpei Bai, Biao Jin, et al.. (2015). Effects of Estradiol Valerate and Remifemin on Norepinephrine Signaling in the Brain of Ovariectomized Rats. Neuroendocrinology. 101(2). 120–132. 21 indexed citations
18.
Cheng, Xiaoxia, et al.. (2015). Association between Menopausal Symptoms and Overactive Bladder: A Cross-Sectional Questionnaire Survey in China. PLoS ONE. 10(10). e0139599–e0139599. 12 indexed citations
19.
Liske, Eckehard, Shuyu Wang, Jianli Liu, et al.. (2014). Effect of Isopropanolic Cimicifuga racemosa Extract on Uterine Fibroids in Comparison with Tibolone among Patients of a Recent Randomized, Double Blind, Parallel‐Controlled Study in Chinese Women with Menopausal Symptoms. Evidence-based Complementary and Alternative Medicine. 2014(1). 717686–717686. 13 indexed citations
20.
Liu, Ping, et al.. (2004). Menopausal depression: comparison of hormone replacement therapy and hormone replacement therapy plus fluoxetine.. PubMed. 117(2). 189–94. 23 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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