Nathan Lay

2.1k citations
54 papers · 1.4k · h-index 18

Impact in

Papers in

Nathan Lay

54 papers receiving 1.4k citations

Peers

Nathan Lay
Comparison fields: 5 of 91
  • Health Informatics 57
  • Radiology, Nuclear Medicine and Imaging 538
  • Pulmonary and Respiratory Medicine 646
  • Computer Vision and Pattern Recognition 324
  • Artificial Intelligence 269
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Dakai Jin United States
Kristen M. Meiburger Italy
Yong Yin China
Dijia Wu China
Adam P. Harrison United States
Jihye Yun South Korea
Lena Costaridou Greece
Theresa Thai United States
Fajin Dong China
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Citations per field
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Citations per year

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-authors

The 25 scholars most cited alongside Nathan Lay, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nathan Lay Line = papers co-authored together Nathan Lay links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018251
2 2017128
3 201999
4 201873
5 201871
6 201760
7 201758
8 202057
9 201353
10 202152
11 201848
12 200743
13 201743
14 201640
15 202231
16 201731
17 202125
18 201918
19 202217
20 201717

About Nathan Lay

Nathan Lay is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering, having authored 54 papers that have together received 1.4k indexed citations. Recurring topics across this work include Prostate Cancer Diagnosis and Treatment (23 papers), Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced Neural Network Applications (8 papers), Renal cell carcinoma treatment (7 papers), Medical Image Segmentation Techniques (7 papers), AI in cancer detection (7 papers), Prostate Cancer Treatment and Research (5 papers) and Medical Imaging and Analysis (4 papers). The work is most often cited by research in Health Informatics (57 citations), Radiology, Nuclear Medicine and Imaging (538 citations), Pulmonary and Respiratory Medicine (646 citations), Computer Vision and Pattern Recognition (324 citations) and Artificial Intelligence (269 citations). Nathan Lay has collaborated with scholars based in United States, Singapore and Türkiye. Frequent co-authors include Ronald M. Summers, Barış Türkbey, Holger R. Roth, Peter A. Pinto, Peter L. Choyke, Le Lü, Bradford J. Wood, Andrew Sohn, Amal Farag and Adam P. Harrison. Their work appears in journals such as Abdominal Radiology, Academic Radiology, American Journal of Roentgenology, Journal of Magnetic Resonance Imaging and The Journal of Urology.

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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