Nathan Lay

2.0k total citations
50 papers, 1.2k citations indexed

About

Nathan Lay is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nathan Lay has authored 50 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Pulmonary and Respiratory Medicine, 29 papers in Radiology, Nuclear Medicine and Imaging and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nathan Lay's work include Radiomics and Machine Learning in Medical Imaging (23 papers), Prostate Cancer Diagnosis and Treatment (23 papers) and MRI in cancer diagnosis (11 papers). Nathan Lay is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (23 papers), Prostate Cancer Diagnosis and Treatment (23 papers) and MRI in cancer diagnosis (11 papers). Nathan Lay collaborates with scholars based in United States, Singapore and Egypt. Nathan Lay's co-authors include Ronald M. Summers, Barış Türkbey, Peter A. Pinto, Peter L. Choyke, Holger R. Roth, Bradford J. Wood, Le Lü, Amal Farag, Adam P. Harrison and Andrew Sohn and has published in prestigious journals such as Radiology, The Journal of Urology and Monthly Weather Review.

In The Last Decade

Nathan Lay

49 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nathan Lay United States 18 747 633 302 262 251 50 1.2k
Duc Fehr United States 10 581 0.8× 366 0.6× 280 0.9× 191 0.7× 228 0.9× 20 991
Nico Karssemeijer Netherlands 19 955 1.3× 645 1.0× 470 1.6× 645 2.5× 255 1.0× 61 1.6k
Cesare Romagnoli Canada 18 368 0.5× 509 0.8× 330 1.1× 137 0.5× 317 1.3× 62 1.0k
Jie‐Zhi Cheng China 19 693 0.9× 272 0.4× 433 1.4× 652 2.5× 133 0.5× 45 1.4k
Senthil Periaswamy United States 13 348 0.5× 248 0.4× 351 1.2× 240 0.9× 100 0.4× 29 834
Guy Nir Canada 14 501 0.7× 307 0.5× 177 0.6× 438 1.7× 253 1.0× 26 966
Eranga Ukwatta Canada 19 540 0.7× 405 0.6× 410 1.4× 126 0.5× 271 1.1× 91 1.2k
Derek W. Cool Canada 17 300 0.4× 395 0.6× 272 0.9× 64 0.2× 299 1.2× 62 837
Lena Costaridou Greece 18 850 1.1× 380 0.6× 506 1.7× 715 2.7× 262 1.0× 86 1.5k
Ester Bonmati United Kingdom 11 564 0.8× 139 0.2× 480 1.6× 204 0.8× 286 1.1× 25 985

Countries citing papers authored by Nathan Lay

Since Specialization
Citations

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

Fields of papers citing papers by Nathan Lay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Lay

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Lay. A scholar is included among the top collaborators of Nathan Lay 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 Nathan Lay. Nathan Lay 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.
Sheikhy, Ali, Fatemeh Dehghani Firouzabadi, Nathan Lay, et al.. (2025). State of the art review of AI in renal imaging. Abdominal Radiology. 50(11). 5305–5323. 2 indexed citations
2.
Haque, Fahmida, Alex Chen, Nathan Lay, et al.. (2025). Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts. Computers in Biology and Medicine. 190. 110052–110052.
3.
Lay, Nathan, Dong Yang, Jesse Tetreault, et al.. (2025). Assessing the Impact of Transition and Peripheral Zone PSA Densities Over Whole‐Gland PSA Density for Prostate Cancer Detection on Multiparametric MRI. The Prostate. 85(6). 612–624. 3 indexed citations
4.
Yılmaz, Enis C., Alex Chen, Nathan Lay, et al.. (2024). Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI. Clinical Imaging. 117. 110363–110363. 3 indexed citations
5.
Belue, Mason J., Enis C. Yılmaz, Yan Mee Law, et al.. (2024). Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI. Abdominal Radiology. 49(8). 2891–2901. 6 indexed citations
6.
Belue, Mason J., Yan Mee Law, Jamie Marko, et al.. (2023). Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation. Academic Radiology. 31(4). 1429–1437. 9 indexed citations
7.
Lin, Yue, Mason J. Belue, Enis C. Yılmaz, et al.. (2023). Deep Learning‐Based T2‐Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates. Journal of Magnetic Resonance Imaging. 59(6). 2215–2223. 12 indexed citations
8.
Anari, Pouria Yazdian, Nathan Lay, Nikhil Gopal, et al.. (2022). An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome. Abdominal Radiology. 47(10). 3554–3562. 10 indexed citations
9.
Mehralivand, Sherif, Dong Yang, Stephanie A. Harmon, et al.. (2022). Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI. Abdominal Radiology. 47(4). 1425–1434. 29 indexed citations
10.
Belue, Mason J., Stephanie A. Harmon, Nathan Lay, et al.. (2022). The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. Journal of the American College of Radiology. 20(2). 134–145. 16 indexed citations
11.
Jin, Chengcheng, Abigail Wong-Rolle, Eric Chen, et al.. (2022). Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma. Journal of Pathology Informatics. 13. 100007–100007. 12 indexed citations
12.
Mehralivand, Sherif, Dong Yang, Stephanie A. Harmon, et al.. (2021). A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging. Academic Radiology. 29(8). 1159–1168. 48 indexed citations
13.
Mehralivand, Sherif, Stephanie A. Harmon, Nathan Lay, et al.. (2021). Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans. IEEE Access. 9. 87531–87542. 22 indexed citations
14.
Shih, Joanna H., Sarah E. Reese, Jeffrey Brender, et al.. (2020). Quality of Prostate MRI: Is the PI-RADS Standard Sufficient?. Academic Radiology. 28(2). 199–207. 56 indexed citations
15.
Greer, Matthew D., Nathan Lay, Joanna H. Shih, et al.. (2018). Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. European Radiology. 28(10). 4407–4417. 73 indexed citations
16.
Gaur, Sonia, Stephanie A. Harmon, Rajan T. Gupta, et al.. (2018). A Multireader Exploratory Evaluation of Individual Pulse Sequence Cancer Detection on Prostate Multiparametric Magnetic Resonance Imaging (MRI). Academic Radiology. 26(1). 5–14. 11 indexed citations
17.
Greer, Matthew D., Joanna H. Shih, Nathan Lay, et al.. (2017). Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2. Radiology. 285(3). 859–869. 123 indexed citations
18.
Tsehay, Yohannes, Nathan Lay, Xiaosong Wang, et al.. (2017). Biopsy-guided learning with deep convolutional neural networks for Prostate Cancer detection on multiparametric MRI. 642–645. 28 indexed citations
19.
Barbu, Adrian & Nathan Lay. (2011). An Introduction to Artificial Prediction Markets. arXiv (Cornell University). 1 indexed citations
20.
Lay, Nathan & Adrian Barbu. (2010). Supervised Aggregation of Classifiers using Artificial Prediction Markets. International Conference on Machine Learning. 591–598. 5 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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