Shichong Zhou

2.1k total citations
63 papers, 1.4k citations indexed

About

Shichong Zhou is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Shichong Zhou has authored 63 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Radiology, Nuclear Medicine and Imaging, 34 papers in Artificial Intelligence and 13 papers in Cancer Research. Recurrent topics in Shichong Zhou's work include AI in cancer detection (29 papers), Radiomics and Machine Learning in Medical Imaging (27 papers) and Thyroid Cancer Diagnosis and Treatment (12 papers). Shichong Zhou is often cited by papers focused on AI in cancer detection (29 papers), Radiomics and Machine Learning in Medical Imaging (27 papers) and Thyroid Cancer Diagnosis and Treatment (12 papers). Shichong Zhou collaborates with scholars based in China, United States and Finland. Shichong Zhou's co-authors include Yi Guo, Cai Chang, Jinhua Yu, Cai Chang, Jun Shi, Yuanyuan Wang, Jin Zhou, Tongtong Liu, Jiawei Li and Yuanyuan Wang and has published in prestigious journals such as Advanced Materials, Nature Communications and Biochemical and Biophysical Research Communications.

In The Last Decade

Shichong Zhou

56 papers receiving 1.4k citations

Peers

Shichong Zhou

RNMI

EDM

CVPR

Longzhong Liu China

Ali Abbasian Ardakani Iran

Georgios Z. Papadakis United States

Rajarsi Gupta United States

Cleo‐Aron Weis Germany

Katarzyna Dobruch‐Sobczak Poland

Yali Zang China

Melcior Sentís Spain

Jiawei Sun China

Yuhui Ma China

Longzhong Liu China

Shichong Zhou

506 ×0.6

RNMI

414 ×0.7

98 ×0.3

EDM

140 ×0.6

217 ×0.9

CVPR

Citations per year, relative to Shichong Zhou Shichong Zhou (= 1×) peers Longzhong Liu

Countries citing papers authored by Shichong Zhou

Since Specialization

Citations

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

Fields of papers citing papers by Shichong Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shichong Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Shichong Zhou. A scholar is included among the top collaborators of Shichong Zhou 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 Shichong Zhou. Shichong Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Z.R., Ruixia Hou, Shiyu Wang, et al.. (2025). FGFBP1 promotes triple-negative breast cancer progression through the KLK10-AKT axis. Biochemical and Biophysical Research Communications. 763. 151763–151763.

Wang, Shiyu, et al.. (2025). BMI and Breast Subcutaneous Fat. Journal of Ultrasound in Medicine. 45(3). 599–610.

Huang, Lihong, Jing Jiao, Shichong Zhou, et al.. (2024). Standardization of ultrasound images across various centers: M2O-DiffGAN bridging the gaps among unpaired multi-domain ultrasound images. Medical Image Analysis. 95. 103187–103187. 2 indexed citations

Jiao, Jing, Jin Zhou, Lihong Huang, et al.. (2024). USFM: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Medical Image Analysis. 96. 103202–103202. 29 indexed citations

Qiao, Liang, Shichong Zhou, Jun Wang, et al.. (2024). Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images. IEEE Transactions on Medical Imaging. 43(7). 2509–2521. 16 indexed citations

Liu, Xihui, et al.. (2024). A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years. Frontiers in Endocrinology. 15. 1323452–1323452. 3 indexed citations

Liu, Chaoxu, et al.. (2023). Application of ultrasound-based radiomics models of breast masses to predict invasive components of encapsulated papillary carcinoma. Quantitative Imaging in Medicine and Surgery. 13(10). 6887–6898. 3 indexed citations

Huang, Qinghua, Zhenkun Lu, Shichong Zhou, et al.. (2023). A novel image-to-knowledge inference approach for automatically diagnosing tumors. Expert Systems with Applications. 229. 120450–120450. 50 indexed citations

Huang, Yunxia, et al.. (2023). Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status. Frontiers in Endocrinology. 14. 1144812–1144812. 21 indexed citations

10.

Yan, Jiaqi, Xiaodong Ma, Meixin Ran, et al.. (2023). An autocatalytic multicomponent DNAzyme nanomachine for tumor-specific photothermal therapy sensitization in pancreatic cancer. Nature Communications. 14(1). 6905–6905. 97 indexed citations

11.

Xi, Jianing, et al.. (2023). An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers. Computers in Biology and Medicine. 155. 106672–106672. 24 indexed citations

12.

Jin, Anqi, et al.. (2022). High Expression of THBS1 Leads to a Poor Prognosis in Papillary Thyroid Cancer and Suppresses the Anti-Tumor Immune Microenvironment. Technology in Cancer Research & Treatment. 21. 2213827248–2213827248. 7 indexed citations

13.

Huang, Qinghua, et al.. (2021). Dense Prediction and Local Fusion of Superpixels: A Framework for Breast Anatomy Segmentation in Ultrasound Image With Scarce Data. IEEE Transactions on Instrumentation and Measurement. 70. 1–8. 50 indexed citations

14.

Zhou, Jin, Anqi Jin, Shichong Zhou, et al.. (2021). Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer. BMC Medical Imaging. 21(1). 184–184. 2 indexed citations

15.

Li, Jiawei, et al.. (2021). Sonographic Features of Triple-Negative Breast Carcinomas Are Correlated With mRNA–lncRNA Signatures and Risk of Tumor Recurrence. Frontiers in Oncology. 10. 587422–587422. 9 indexed citations

16.

Yu, Jinhua, Yinhui Deng, Tongtong Liu, et al.. (2020). Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics. Nature Communications. 11(1). 4807–4807. 200 indexed citations

17.

Zhou, Jin, Shichong Zhou, Jiawei Li, et al.. (2019). Risk factors of central neck lymph node metastasis following solitary papillary thyroid carcinoma. Zhonghua chaosheng yingxiangxue zazhi. 28(3). 235–240. 3 indexed citations

18.

Zhou, Jin, Shichong Zhou, Jiawei Li, et al.. (2019). Risk factors of predicting lateral neck lymph node metastasis following solitary papillary thyroid carcinoma. Zhonghua chaosheng yingxiangxue zazhi. 28(11). 971–975. 1 indexed citations

19.

Zhou, Shichong, et al.. (2017). The study of ultrasound guided fine needle aspiration of axillary lymph nodes in breast cancer. Zhonghua chaosheng yingxiangxue zazhi. 26(6). 527–530. 1 indexed citations

20.

Li, Jiawei, Cai Chang, Min Chen, et al.. (2016). Identification of calcifications in thyroid nodules: comparison between ultrasonography and CT. Zhonghua chaosheng yingxiangxue zazhi. 25(5). 384–387. 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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