Di Ou

1.3k total citations
52 papers, 985 citations indexed

About

Di Ou is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Di Ou has authored 52 papers receiving a total of 985 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 13 papers in Endocrinology, Diabetes and Metabolism and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Di Ou's work include Thyroid Cancer Diagnosis and Treatment (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (6 papers). Di Ou is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (6 papers). Di Ou collaborates with scholars based in China, United States and Canada. Di Ou's co-authors include Esteban Celis, L. Ötvös, S Gillam, Thomas M. Seed, H S Teh, Robert W. Olafson, James Alexander, Mark D. Wiederhold, Armando Jardim and James Harris and has published in prestigious journals such as The Journal of Experimental Medicine, SHILAP Revista de lepidopterología and The Journal of Immunology.

In The Last Decade

Di Ou

48 papers receiving 948 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Di Ou China 18 253 236 219 137 124 52 985
Shih‐Chieh Chang Taiwan 21 153 0.6× 483 2.0× 95 0.4× 116 0.8× 159 1.3× 77 1.5k
Na Lei China 21 301 1.2× 422 1.8× 131 0.6× 68 0.5× 151 1.2× 82 1.3k
Dae Yong Kim South Korea 22 117 0.5× 471 2.0× 212 1.0× 62 0.5× 133 1.1× 53 1.4k
Liana Verinaud Brazil 21 188 0.7× 311 1.3× 362 1.7× 46 0.3× 71 0.6× 61 1.1k
Patrick J. Murphy United States 22 228 0.9× 527 2.2× 484 2.2× 80 0.6× 94 0.8× 51 1.7k
Amrisha Verma United States 23 102 0.4× 879 3.7× 411 1.9× 83 0.6× 127 1.0× 41 1.7k
Y. Graus Netherlands 18 264 1.0× 307 1.3× 481 2.2× 83 0.6× 48 0.4× 32 1.3k
Arif Uddin India 20 132 0.5× 1.0k 4.4× 160 0.7× 109 0.8× 92 0.7× 68 1.7k
Anna Lisowska Poland 20 157 0.6× 272 1.2× 107 0.5× 48 0.4× 75 0.6× 107 1.1k
Ling Wei China 17 259 1.0× 262 1.1× 114 0.5× 47 0.3× 73 0.6× 76 996

Countries citing papers authored by Di Ou

Since Specialization
Citations

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

Fields of papers citing papers by Di Ou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Di Ou

This figure shows the co-authorship network connecting the top 25 collaborators of Di Ou. A scholar is included among the top collaborators of Di Ou 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 Di Ou. Di Ou 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.
Xu, Wenxia, Di Ou, Yufei Zhao, et al.. (2025). Curcumin alleviates lipopolysaccharide-induced alveolar epithelial Glycocalyx damage by modulating the Rac1/NF-κB/HPSE pathway. Cellular Signalling. 134. 111909–111909.
2.
Yao, Jincao, Ni Chen, Sohaib Asif, et al.. (2024). Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma. Endocrine. 87(3). 1060–1069. 3 indexed citations
3.
Yao, Jincao, Chen Chen, Yifan Wang, et al.. (2024). Deep learning-assisted distinguishing breast phyllodes tumours from fibroadenomas based on ultrasound images: a diagnostic study. British Journal of Radiology. 97(1163). 1816–1825.
4.
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6.
Yao, Jincao, Chen Cui, Liping Wang, et al.. (2023). A new xAI framework with feature explainability for tumors decision-making in Ultrasound data: comparing with Grad-CAM. Computer Methods and Programs in Biomedicine. 235. 107527–107527. 33 indexed citations
7.
Chen, Chen, Jincao Yao, Di Ou, et al.. (2023). Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study. European Radiology. 34(4). 2323–2333. 11 indexed citations
8.
Yao, Jincao, Yanming Zhang, Jiafei Shen, et al.. (2023). AI diagnosis of Bethesda category IV thyroid nodules. iScience. 26(11). 108114–108114. 9 indexed citations
9.
Shen, Jiafei, Long Chen, Di Ou, et al.. (2023). Prognosis and influencing factors of follicular thyroid cancer. Cancer Medicine. 13(1). e6727–e6727. 9 indexed citations
10.
Chen, Ni, et al.. (2023). Value of deep learning models based on ultrasonic dynamic videos for distinguishing thyroid nodules. Frontiers in Oncology. 12. 1066508–1066508. 4 indexed citations
11.
Zhang, Yan, Dou Du, Chao Fang, et al.. (2022). Epigenetics disruptions enabled by porphyrin-derived metal-organic frameworks disarm resistances to sonocatalytic ROS anti-tumor actions. Fundamental Research. 5(1). 296–306. 18 indexed citations
12.
13.
Yao, Jincao, Wenwen Yue, Wei Li, et al.. (2022). DeepThy‐Net: A Multimodal Deep Learning Method for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer. SHILAP Revista de lepidopterología. 4(10). 33 indexed citations
14.
Yang, Chen, et al.. (2020). Ultrasonic identification and regression analysis of 294 thyroid follicular tumors. Journal of Cancer Research and Therapeutics. 16(5). 1056–1056. 9 indexed citations
15.
Anderson, K.M., Waddah A. Alrefai, Pradeep K. Dudeja, et al.. (2002). Increased cytosol Ca2+ and type 1 programmed cell death in Bcl-2-positive U937 but not in Bcl-2-negative PC-3 and Panc-1 cells induced by the 5-lipoxygenase inhibitor MK 886. Prostaglandins Leukotrienes and Essential Fatty Acids. 66(4). 443–452. 10 indexed citations
16.
Ou, Di, et al.. (1992). Analysis of T- and B-cell epitopes of capsid protein of rubella virus by using synthetic peptides. Journal of Virology. 66(3). 1674–1681. 32 indexed citations
17.
Shen, Mingli, et al.. (1991). Immunomopharmacological effects of polysaccharides from Acanthopanax senticosus on experimental animals. International Journal of Immunopharmacology. 13(5). 549–554. 33 indexed citations
18.
Jardim, Armando, James Alexander, H S Teh, Di Ou, & Robert W. Olafson. (1990). Immunoprotective Leishmania major synthetic T cell epitopes.. The Journal of Experimental Medicine. 172(2). 645–648. 88 indexed citations
19.
Celis, Esteban, Di Ou, & L. Ötvös. (1988). Recognition of hepatitis B surface antigen by human T lymphocytes. Proliferative and cytotoxic responses to a major antigenic determinant defined by synthetic peptides.. The Journal of Immunology. 140(6). 1808–1815. 94 indexed citations
20.
Ou, Di, et al.. (1988). Variable Response to Intravenous γ-Globulin Therapy in a Patient With Autoimmune Neutropenia, Thrombocytopenia, and Pemphigoid. Southern Medical Journal. 81(5). 653–655. 2 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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