Lee Cooper

17.2k total citations · 2 hit papers
117 papers, 4.4k citations indexed

About

Lee Cooper is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Lee Cooper has authored 117 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Artificial Intelligence, 33 papers in Radiology, Nuclear Medicine and Imaging and 31 papers in Computer Vision and Pattern Recognition. Recurrent topics in Lee Cooper's work include AI in cancer detection (39 papers), Cell Image Analysis Techniques (28 papers) and Radiomics and Machine Learning in Medical Imaging (21 papers). Lee Cooper is often cited by papers focused on AI in cancer detection (39 papers), Cell Image Analysis Techniques (28 papers) and Radiomics and Machine Learning in Medical Imaging (21 papers). Lee Cooper collaborates with scholars based in United States, United Kingdom and Philippines. Lee Cooper's co-authors include Daniel J. Brat, David A. Gutman, Mohamed Amgad, Joel Saltz, Jun Kong, Safoora Yousefi, Pooya Mobadersany, Tahsin Kurç, José E. Velázquez Vega and Jill S. Barnholtz‐Sloan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Communications.

In The Last Decade

Lee Cooper

111 papers receiving 4.3k citations

Hit Papers

align trajectories

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.

Predicting cancer outcomes from histology and genomics using convolutional networks

2018 646 citations Pooya Mobadersany, Safoora Yousefi et al. Proceedings of the National Academy of Sciences profile →
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

2019 272 citations Lee Cooper et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lee Cooper United States	34	1.5k	1.3k	1.1k	811	663	117	4.4k
Tahsin Kurç United States	34	1.3k 0.9×	2.0k 1.5×	613 0.6×	204 0.3×	352 0.5×	219	4.9k
Olivier Gevaert United States	47	3.3k 2.1×	1.4k 1.0×	2.1k 2.0×	909 1.1×	1.2k 1.8×	180	7.6k
Arvind Rao United States	32	1.5k 1.0×	503 0.4×	884 0.8×	737 0.9×	625 0.9×	131	4.0k
Alexander T. Pearson United States	32	1.2k 0.8×	1.2k 0.9×	732 0.7×	124 0.2×	677 1.0×	159	4.1k
Rivka R. Colen United States	34	2.1k 1.4×	653 0.5×	450 0.4×	1.4k 1.7×	410 0.6×	138	4.2k
Timo Gaiser Germany	28	868 0.6×	837 0.6×	1.1k 1.1×	128 0.2×	625 0.9×	134	3.7k
Marilyn M. Bui United States	44	1.3k 0.8×	1.2k 0.9×	1.6k 1.6×	121 0.1×	1.2k 1.8×	192	6.1k
Matija Snuderl United States	40	1.7k 1.1×	1.2k 0.9×	3.2k 3.0×	2.6k 3.2×	2.2k 3.3×	188	9.2k
Raymond Y. Huang United States	30	2.5k 1.6×	482 0.4×	556 0.5×	1.7k 2.0×	358 0.5×	122	4.8k
Kaustav Bera United States	25	2.7k 1.8×	1.0k 0.8×	250 0.2×	281 0.3×	388 0.6×	91	3.7k

Countries citing papers authored by Lee Cooper

Since Specialization

Citations

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

Fields of papers citing papers by Lee Cooper

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lee Cooper

This figure shows the co-authorship network connecting the top 25 collaborators of Lee Cooper. A scholar is included among the top collaborators of Lee Cooper 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 Lee Cooper. Lee Cooper 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

Li, Eric V., Yi Ren, Rikiya Yamashita, et al.. (2025). An Artificial Intelligence–Digital Pathology Algorithm Predicts Survival After Radical Prostatectomy From the Prostate, Lung, Colorectal, and Ovarian Cancer Trial. The Journal of Urology. 213(5). 600–608.

Clarke, Emily J., et al.. (2025). Inflammation and response to bacterial infection as potential drivers of equine odontoclastic tooth resorption and hypercementosis: A proteomics insight. Equine Veterinary Journal. 57(4). 977–990.

Sheriff, Sulaiman, Brent D. Weinberg, Lee Cooper, et al.. (2025). NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets. Radiology Artificial Intelligence. 7(2). e230579–e230579. 2 indexed citations

Morales-Álvarez, Pablo, Lee Cooper, Jeffrey A. Goldstein, et al.. (2024). Learning from crowds for automated histopathological image segmentation. Computerized Medical Imaging and Graphics. 112. 102327–102327. 5 indexed citations

Nguyen, Minh, et al.. (2024). Quantitative Modeling to Characterize Maternal Inflammatory Response of Histologic Chorioamnionitis in Placental Membranes. American Journal of Reproductive Immunology. 92(4). e13944–e13944. 2 indexed citations

Ross, Ashley E., et al.. (2024). Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology. Modern Pathology. 37(3). 100422–100422. 3 indexed citations

Lewis, Joshua E., Lee Cooper, David L. Jaye, & Olga Pozdnyakova. (2023). Automated Machine Learning-Based Diagnosis and Molecular Characterization of Acute Leukemias using Flow Cytometry Data. American Journal of Clinical Pathology. 160(Supplement_1). S119–S120.

Goldstein, Jeffrey A., et al.. (2023). Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus. Placenta. 135. 43–50. 9 indexed citations

Amgad, Mohamed, James M. Hodge, Maha AT Elsebaie, et al.. (2023). A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer. Nature Medicine. 30(1). 85–97. 53 indexed citations

10.

Lewis, Joshua E., Lee Cooper, David L. Jaye, & Olga Pozdnyakova. (2023). Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry. Modern Pathology. 37(1). 100373–100373. 14 indexed citations

11.

Mobadersany, Pooya, Lee Cooper, & Jeffrey A. Goldstein. (2021). GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images. Laboratory Investigation. 101(7). 942–951. 27 indexed citations

12.

Lee, Sanghoon, Mohamed Amgad, Pooya Mobadersany, et al.. (2020). Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers. Cancer Research. 81(4). 1171–1177. 14 indexed citations

13.

Amgad, Mohamed, et al.. (2019). Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Laboratory Investigation. 100(1). 98–109. 74 indexed citations

14.

Mobadersany, Pooya, Safoora Yousefi, Mohamed Amgad, et al.. (2018). Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences. 115(13). E2970–E2979. 646 indexed citations breakdown →

15.

Gutman, David A., Mohammed Khalilia, Sanghoon Lee, et al.. (2017). The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research. Cancer Research. 77(21). e75–e78. 100 indexed citations

16.

Patel, Sohil H., Laila Poisson, Daniel J. Brat, et al.. (2017). T2–FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project. Clinical Cancer Research. 23(20). 6078–6085. 283 indexed citations

17.

Gutman, David A., William D. Dunn, Patrick Großmann, et al.. (2015). Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology. 57(12). 1227–1237. 73 indexed citations

18.

Kurç, Tahsin, Xin Qi, Fusheng Wang, et al.. (2015). Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies. BMC Bioinformatics. 16(1). 399–399. 14 indexed citations

19.

Teodoro, George, Tahsin Kurç, Tony Pan, et al.. (2012). Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems. PubMed. 2012. 1093–1104. 23 indexed citations

20.

Cooper, Lee, et al.. (1996). DESIGNING THE CONTROL AND SIMULATION OF EGR. 21(1). 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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