Pingjun Chen

1.5k total citations
23 papers, 629 citations indexed

About

Pingjun Chen is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pingjun Chen has authored 23 papers receiving a total of 629 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 9 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pingjun Chen's work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Chronic Lymphocytic Leukemia Research (3 papers). Pingjun Chen is often cited by papers focused on AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Chronic Lymphocytic Leukemia Research (3 papers). Pingjun Chen collaborates with scholars based in United States, China and Oman. Pingjun Chen's co-authors include Lin Yang, Xiaoshuang Shi, Kyle D. Allen, Linlin Gao, Zizhao Zhang, Fuyong Xing, Yuanpu Xie, Hai Su, Jinzheng Cai and Nazeel Ahmad and has published in prestigious journals such as Blood, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Pingjun Chen

21 papers receiving 612 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pingjun Chen United States 11 284 239 176 145 83 23 629
Zhehao Dai China 16 176 0.6× 189 0.8× 166 0.9× 122 0.8× 70 0.8× 35 690
Krzysztof J. Geras United States 11 531 1.9× 569 2.4× 84 0.5× 81 0.6× 106 1.3× 28 907
Albert Comelli Italy 23 197 0.7× 710 3.0× 168 1.0× 46 0.3× 265 3.2× 67 1.2k
Donato Cascio Italy 15 259 0.9× 323 1.4× 199 1.1× 78 0.5× 65 0.8× 48 664
Sohrab Afshari Mirak United States 15 143 0.5× 438 1.8× 136 0.8× 154 1.1× 90 1.1× 24 876
Yanlin Tan China 17 201 0.7× 224 0.9× 135 0.8× 32 0.2× 62 0.7× 36 633
Neslihan Bayramoğlu Finland 7 343 1.2× 261 1.1× 274 1.6× 32 0.2× 62 0.7× 17 551
Thomas de Bel Netherlands 10 641 2.3× 459 1.9× 231 1.3× 19 0.1× 66 0.8× 16 1.0k
Jiawei Tian China 18 334 1.2× 382 1.6× 138 0.8× 25 0.2× 109 1.3× 54 914
Rajendra S. Sonawane India 11 136 0.5× 112 0.5× 72 0.4× 89 0.6× 21 0.3× 20 539

Countries citing papers authored by Pingjun Chen

Since Specialization
Citations

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

Fields of papers citing papers by Pingjun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pingjun Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Pingjun Chen. A scholar is included among the top collaborators of Pingjun Chen 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 Pingjun Chen. Pingjun Chen 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.
Ercan, Caner, et al.. (2025). 1354 AI-STORM: AI-Guided Sampling Tissues for Optimal Representative Morphology. Laboratory Investigation. 105(3). 103592–103592.
2.
Zafar, Anas, Rizwan Qureshi, Xinqi Fan, et al.. (2024). Single Stage Adaptive Multi-Attention Network for Image Restoration. IEEE Transactions on Image Processing. 33. 2924–2935. 12 indexed citations
3.
Hussein, Siba El, Pingjun Chen, L. Jeffrey Medeiros, et al.. (2022). Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia. Modern Pathology. 35(8). 1121–1125. 7 indexed citations
4.
Chen, Pingjun, Jianjun Zhang, & Jia Wu. (2022). Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.. PubMed. 2(3). 5 indexed citations
5.
Chen, Pingjun, Muhammad Aminu, Siba El Hussein, Joseph D. Khoury, & Jia Wu. (2021). CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology. Software Impacts. 10. 100156–100156.
6.
Hussein, Siba El, Pingjun Chen, L. Jeffrey Medeiros, et al.. (2021). Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia. The Journal of Pathology. 256(1). 4–14. 16 indexed citations
7.
Chen, Pingjun, Yun Liang, Xiaoshuang Shi, Lin Yang, & Paul Gader. (2021). Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion. Neurocomputing. 453. 312–325. 13 indexed citations
8.
Hussein, Siba El, Pingjun Chen, L. Jeffrey Medeiros, Jia Wu, & Joseph D. Khoury. (2021). Artificial Intelligence-Assisted Mapping of Proliferation Centers in Chronic Lymphocytic Leukemia/ Small Lymphocytic Lymphoma Identifies Patterns That Reliably Distinguish Accelerated Phase and Large Cell Transformation. Blood. 138(Supplement 1). 1558–1558. 1 indexed citations
9.
Chen, Pingjun, Xiaoshuang Shi, Yun Liang, et al.. (2020). Interactive thyroid whole slide image diagnostic system using deep representation. Computer Methods and Programs in Biomedicine. 195. 105630–105630. 26 indexed citations
10.
Li, Yuan, Pingjun Chen, Zhiyuan Li, et al.. (2020). Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning. Artificial Intelligence in Medicine. 108. 101918–101918. 27 indexed citations
11.
Chen, Pingjun, Jinzheng Cai, & Lin Yang. (2020). Chromosome Segmentation via Data Simulation and Shape Learning. PubMed. 2020. 1637–1640. 5 indexed citations
12.
Shi, Xiaoshuang, Fuyong Xing, Kaidi Xu, et al.. (2020). Loss-Based Attention for Interpreting Image-Level Prediction of Convolutional Neural Networks. IEEE Transactions on Image Processing. 30. 1662–1675. 28 indexed citations
13.
Xing, Fuyong, Yuanpu Xie, Xiaoshuang Shi, et al.. (2019). Towards pixel-to-pixel deep nucleus detection in microscopy images. BMC Bioinformatics. 20(1). 472–472. 30 indexed citations
14.
Chen, Pingjun, Linlin Gao, Xiaoshuang Shi, Kyle D. Allen, & Lin Yang. (2019). Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Computerized Medical Imaging and Graphics. 75. 84–92. 185 indexed citations
15.
Chen, Pingjun & Lin Yang. (2019). tissueloc: Whole slide digital pathology image tissue localization. The Journal of Open Source Software. 4(33). 1148–1148. 5 indexed citations
16.
Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(6). 289–289. 2 indexed citations
17.
Zhang, Zizhao, Pingjun Chen, Xiaoshuang Shi, & Lin Yang. (2019). Text-Guided Neural Network Training for Image Recognition in Natural Scenes and Medicine. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(5). 1733–1745. 29 indexed citations
18.
Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(5). 236–245. 189 indexed citations
19.
Chen, Pingjun. (2018). Knee Osteoarthritis Severity Grading Dataset. Data Archiving and Networked Services (DANS). 1. 32 indexed citations
20.
Chen, Pingjun, et al.. (2015). Fiber segmentation using a density-peaks clustering algorithm. 633–637. 4 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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