Scott Doyle

3.8k citations
89 papers · 3.0k · h-index 27

Impact in

Papers in

Scott Doyle

85 papers receiving 2.9k citations

Peers

Scott Doyle
Comparison fields: 5 of 147
  • Biophysics 280
  • Transportation 271
  • Artificial Intelligence 1.3k
  • Computer Vision and Pattern Recognition 746
  • Urology 163
Replace Lisa Tang with:
Lisa Tang Canada
Ming Y. Lu United States
Sara Moccia Italy
Xiyue Wang China
Chiun‐Sheng Huang Taiwan
Ying Zhuge United States
George C. Linderman United States
Arunachalam Narayanaswamy United States
Shengli Li China
Bowen Wang China
Scott Doyle relative to Lisa Tang Canada Lisa Tang's profile →
Citations per field
00.5×11.3×
Lisa Tang · 1×
Citations per year

Countries citing papers authored by Scott Doyle

Since Specialization
Citations

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

Fields of papers citing papers by Scott Doyle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Scott Doyle, 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 Scott Doyle Line = papers co-authored together Scott Doyle links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 89 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008260
2 2006250
3 2010226
4 2008217
5 2014208
6 2007186
7 2018160
8 2004127
9 2011105
10 200896
11 200691
12 201285
13 200384
14 201878
15 201156
16 201852
17 201051
18 201647
19 200437
20 200936

About Scott Doyle

Scott Doyle is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Surgery and Molecular Biology, having authored 89 papers that have together received 3.0k indexed citations. Recurring topics across this work include AI in cancer detection (31 papers), Radiomics and Machine Learning in Medical Imaging (12 papers), Medical Image Segmentation Techniques (9 papers), Digital Imaging for Blood Diseases (8 papers), Gene expression and cancer classification (7 papers), Medical Imaging and Analysis (6 papers), Asthma and respiratory diseases (5 papers) and Image Retrieval and Classification Techniques (5 papers). The work is most often cited by research in Biophysics (280 citations), Transportation (271 citations), Artificial Intelligence (1.3k citations), Computer Vision and Pattern Recognition (746 citations) and Urology (163 citations). Scott Doyle has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Anant Madabhushi, Michael D. Feldman, John Tomaszewski, Shannon C. Agner, Marc Schlossberg, Jean Stockard, John Tomaszeweski, Roger Kirby, Mark Speakman and Christos V. Ioannou. Their work appears in journals such as Value in Health, Journal of Pathology Informatics, Journal of Pediatric Orthopaedics, The Laryngoscope and British 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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