William E. Karnes

32 papers receiving 1.2k citations

William E. Karnes's Hit Papers

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy 2018 · 579 citations
5790+2+5Years since publication100200300400500

Peers

William E. Karnes
Comparison fields: 5 of 88
  • Health Informatics 45
  • Oncology 660
  • Gastroenterology 84
  • Radiology, Nuclear Medicine and Imaging 298
  • Pulmonary and Respiratory Medicine 281
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Yushi Ogawa Japan
Xun Xiao China
Fei Xiong China
Raymond Kelly Ireland
Bing Ren United States
Alessandro Caputo Italy
Tomohiro Tsuchida Japan
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Yunchao Deng China
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Countries citing papers authored by William E. Karnes

Since Specialization
Citations

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

Fields of papers citing papers by William E. Karnes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William E. Karnes. 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 William E. Karnes. The network helps show where William E. Karnes may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy
Hit paper breakdown →
2018579
2 1992161
3 201998
4 202058
5 202056
6 201652
7 202137
8 199336
9 202223
10 201322
11
Autocrine growth factors and solid tumor malignancy.
199118
12 200312
13 20214
14 20193
15 20213
16 20173
17 20193
18 20183
19 20223
20 20203

About William E. Karnes

William E. Karnes is a scholar working on Oncology, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Surgery and Pathology and Forensic Medicine, having authored 33 papers that have together received 1.2k indexed citations. Recurring topics across this work include Colorectal Cancer Screening and Detection (23 papers), Gastric Cancer Management and Outcomes (5 papers), AI in cancer detection (2 papers), Esophageal and GI Pathology (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Glycosylation and Glycoproteins Research (1 paper), Nutritional Studies and Diet (1 paper) and Cancer Cells and Metastasis (1 paper). The work is most often cited by research in Health Informatics (45 citations), Oncology (660 citations), Gastroenterology (84 citations), Radiology, Nuclear Medicine and Imaging (298 citations) and Pulmonary and Respiratory Medicine (281 citations). William E. Karnes has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Talal Alkayali, Farid Jalali, Priyam V. Tripathi, Gregor Urban, Pierre Baldi, Mohit Mittal, John Henry Walsh, Mary Territo, Jason Samarasena and James Requa. Their work appears in journals such as The American Journal of Gastroenterology, Gastrointestinal Endoscopy, Gastroenterology, Digestive Diseases and Sciences and American Journal of Physiology-Gastrointestinal and Liver Physiology.

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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