Bing Ren

2.1k total citations · 1 hit paper
26 papers, 1.5k citations indexed

About

Bing Ren is a scholar working on Oncology, Surgery and Artificial Intelligence. According to data from OpenAlex, Bing Ren has authored 26 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Oncology, 8 papers in Surgery and 7 papers in Artificial Intelligence. Recurrent topics in Bing Ren's work include Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (6 papers) and Colorectal Cancer Screening and Detection (5 papers). Bing Ren is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (6 papers) and Colorectal Cancer Screening and Detection (5 papers). Bing Ren collaborates with scholars based in United States, China and Lebanon. Bing Ren's co-authors include Jolyon Terragni, Richard A. Young, Brian David Dynlacht, Thomas L. Volkert, Yasuhiko Takahashi, Arief A. Suriawinata, Xiaoying Liu, Saeed Hassanpour, Fyza Y. Shaikh and Weijing Sun and has published in prestigious journals such as Journal of Clinical Oncology, Genes & Development and SHILAP Revista de lepidopterología.

In The Last Decade

Bing Ren

24 papers receiving 1.4k citations

Hit Papers

E2F integrates cell cycle progression with DNA repair, re... 2002 2026 2010 2018 2002 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. E2F integrates cell cycle progression with DNA repair, replication, and G 2 /M checkpoints
    2002 931 citations Bing Ren, Yasuhiko Takahashi et al. Genes & Development profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bing Ren United States 12 833 709 201 181 178 26 1.5k
Robin Edwards United States 12 493 0.6× 492 0.7× 219 1.1× 220 1.2× 195 1.1× 25 1.1k
Bárbara Pesce Chile 17 1.2k 1.4× 527 0.7× 215 1.1× 492 2.7× 291 1.6× 37 2.2k
Ichiro Maeda Japan 21 772 0.9× 355 0.5× 300 1.5× 109 0.6× 114 0.6× 80 1.6k
Anthony O’Grady Ireland 22 527 0.6× 435 0.6× 208 1.0× 158 0.9× 45 0.3× 54 1.2k
Yalai Bai United States 20 455 0.5× 426 0.6× 298 1.5× 122 0.7× 87 0.5× 37 1.1k
Farah Khalil United States 15 315 0.4× 663 0.9× 144 0.7× 214 1.2× 157 0.9× 37 1.3k
Friedrich Feuerhake Germany 21 433 0.5× 395 0.6× 204 1.0× 156 0.9× 112 0.6× 89 1.6k
Emiel A. M. Janssen Norway 24 523 0.6× 576 0.8× 488 2.4× 133 0.7× 93 0.5× 59 1.7k
Benjamin C. Calhoun United States 20 529 0.6× 357 0.5× 387 1.9× 121 0.7× 81 0.5× 72 1.3k
Naohiro Uraoka Japan 18 602 0.7× 484 0.7× 330 1.6× 210 1.2× 40 0.2× 48 1.1k

Countries citing papers authored by Bing Ren

Since Specialization
Citations

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

Fields of papers citing papers by Bing Ren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bing Ren

This figure shows the co-authorship network connecting the top 25 collaborators of Bing Ren. A scholar is included among the top collaborators of Bing Ren 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 Bing Ren. Bing Ren 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.
Tomita, Naofumi, et al.. (2025). Cross-Modality Learning for Predicting Immunohistochemistry Biomarkers from Hematoxylin and Eosin–Stained Whole Slide Images. American Journal Of Pathology. 195(12). 2400–2410.
2.
Ren, Bing, Mingjie Cai, & Bin Yu. (2025). Multi-Dueling framework for multi-agent reinforcement learning. Applied Soft Computing. 181. 113464–113464.
3.
Tomita, Naofumi, et al.. (2025). Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology. American Journal Of Pathology. 195(4). 680–689. 1 indexed citations
4.
5.
6.
Levy, Joshua, Mustafa Nasir-Moin, Fred Kolling, et al.. (2023). Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis. American Journal Of Pathology. 193(6). 778–795. 11 indexed citations
7.
Abdollahi, Behnaz, Manu Goyal, Matthew A. Suriawinata, et al.. (2022). Bladder cancer prognosis using deep neural networks and histopathology images. Journal of Pathology Informatics. 13. 100135–100135. 19 indexed citations
8.
9.
Schned, Alan R., et al.. (2021). Complaint of Testicular Discomfort in Patient with Esophageal Adenocarcinoma. SHILAP Revista de lepidopterología. 2021. 1–3. 1 indexed citations
10.
11.
Wei, Jerry, Arief A. Suriawinata, Bing Ren, et al.. (2021). Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification. 2472–2482. 34 indexed citations
12.
Levy, Joshua, Arief A. Suriawinata, Xiaoying Liu, et al.. (2020). A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies. Modern Pathology. 34(4). 808–822. 29 indexed citations
13.
Rezaee, Michael E., et al.. (2020). Mischievous malakoplakia: A potential pitfall of mpMRI of the prostate?. Urology Case Reports. 32. 101222–101222. 1 indexed citations
14.
Yin, Feng, Hao Xie, Jingmei Lin, et al.. (2020). Validation of American Joint Committee on Cancer 8th edition of TNM staging in resected distal pancreatic cancer. World Journal of Gastrointestinal Pharmacology and Therapeutics. 11(2). 25–39. 6 indexed citations
15.
Wei, Jason, Jerry Wei, Christopher R. Jackson, et al.. (2019). Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach. Journal of Pathology Informatics. 10(1). 7–7. 46 indexed citations
16.
Ren, Bing, Xiaoying Liu, & Arief A. Suriawinata. (2018). Pancreatic Ductal Adenocarcinoma and Its Precursor Lesions. American Journal Of Pathology. 189(1). 9–21. 59 indexed citations
17.
Ma, Changqing, Krishna Patel, Fyza Y. Shaikh, et al.. (2016). Frequent programmed death-ligand 1 expression in gastric cancer associated with Epstein-Barr virus or microsatellite instability: Implications for predictive biomarker development.. Journal of Clinical Oncology. 34(15_suppl). e15579–e15579. 1 indexed citations
18.
Ma, Changqing, Krishna Patel, Aatur D. Singhi, et al.. (2016). Programmed Death-Ligand 1 Expression Is Common in Gastric Cancer Associated With Epstein-Barr Virus or Microsatellite Instability. The American Journal of Surgical Pathology. 40(11). 1496–1506. 130 indexed citations
19.
20.
Ren, Bing, Yasuhiko Takahashi, Thomas L. Volkert, et al.. (2002). E2F integrates cell cycle progression with DNA repair, replication, and G 2 /M checkpoints. Genes & Development. 16(2). 245–256. 931 indexed citations breakdown →

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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