Ralph Highnam

51 papers receiving 1.3k citations

Peers

Ralph Highnam
Comparison fields: 5 of 85
  • Artificial Intelligence 876
  • Pulmonary and Respiratory Medicine 840
  • Radiology, Nuclear Medicine and Imaging 539
  • Oncology 445
  • Computer Vision and Pattern Recognition 272
Replace Nicolas Michoux with:
Nicolas Michoux Belgium
Marios A. Gavrielides United States
Elodia B. Cole United States
Shidan Wang United States
Yali Zang China
Kathryn F. O′Shaughnessy United States
Guangzhi Ma China
Sergi Ganau Spain
Longzhong Liu China
Ralph Highnam relative to Nicolas Michoux Belgium Nicolas Michoux's profile →
Citations per field
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Citations per year

Countries citing papers authored by Ralph Highnam

Since Specialization
Citations

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

Fields of papers citing papers by Ralph Highnam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999137
2 1997126
3
Robust breast composition measurement - Volpara™
201080
4 201076
5 199670
6 201663
7 200162
8 200656
9 200854
10 200752
11 201450
12 200850
13 200643
14 199741
15 200737
16 200735
17 201333
18 201533
19 199826
20 200126

About Ralph Highnam

Ralph Highnam is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Oncology and Computer Vision and Pattern Recognition, having authored 52 papers that have together received 1.4k indexed citations. Recurring topics across this work include AI in cancer detection (35 papers), Digital Radiography and Breast Imaging (34 papers), Global Cancer Incidence and Screening (15 papers), Medical Imaging Techniques and Applications (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Medical Image Segmentation Techniques (6 papers), Breast Cancer Treatment Studies (5 papers) and Infrared Thermography in Medicine (5 papers). The work is most often cited by research in Artificial Intelligence (876 citations), Pulmonary and Respiratory Medicine (840 citations), Radiology, Nuclear Medicine and Imaging (539 citations), Oncology (445 citations) and Computer Vision and Pattern Recognition (272 citations). Ralph Highnam has collaborated with scholars based in United Kingdom, New Zealand and Japan. Frequent co-authors include Michael Brady, B. J. Shepstone, J. Michael Brady, Ruth Warren, Mona Jeffreys, George Davey Smith, Yasuyo Kita, Valerie McCormack, Martin J. Yaffe and Isabel dos‐Santos‐Silva. Their work appears in journals such as IEEE Transactions on Medical Imaging, European Journal of Radiology, British Journal of Radiology, Cancer Epidemiology Biomarkers & Prevention and Medical Image Analysis.

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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