Michael Berks

44 papers receiving 448 citations

Peers

Michael Berks
Comparison fields: 5 of 58
  • Pathology and Forensic Medicine 165
  • Dermatology 73
  • Radiology, Nuclear Medicine and Imaging 121
  • Pulmonary and Respiratory Medicine 82
  • Biophysics 13
Replace Pat W. Whitworth with:
Pat W. Whitworth United States
Ermelinda Bonaccio United States
Christiane Marx Germany
Delia M. Keating United States
Rasha Kamal Egypt
Markus Müller‐Schimpfle Germany
John C. Moad United States
Fatma Tokat Türkiye
Hiroko Tsunoda Japan
Edward Cronin United States
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Citations per field
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Citations per year

Countries citing papers authored by Michael Berks

Since Specialization
Citations

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

Fields of papers citing papers by Michael Berks

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201439
2 201938
3 202034
4 201830
5 201729
6 202327
7 202123
8 201822
9 202321
10 201719
11 201118
12 202014
13 202012
14 202211
15 201210
16 202110
17 20089
18 20108
19 20237
20 20227

About Michael Berks

Michael Berks is a scholar working on Artificial Intelligence, Pathology and Forensic Medicine, Pulmonary and Respiratory Medicine, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 50 papers that have together received 457 indexed citations. Recurring topics across this work include AI in cancer detection (17 papers), Systemic Sclerosis and Related Diseases (16 papers), Digital Radiography and Breast Imaging (12 papers), Global Cancer Incidence and Screening (9 papers), MRI in cancer diagnosis (6 papers), Advanced MRI Techniques and Applications (4 papers), Medical Image Segmentation Techniques (3 papers) and Dermatologic Treatments and Research (3 papers). The work is most often cited by research in Pathology and Forensic Medicine (165 citations), Dermatology (73 citations), Radiology, Nuclear Medicine and Imaging (121 citations), Pulmonary and Respiratory Medicine (82 citations) and Biophysics (13 citations). Michael Berks has collaborated with scholars based in United Kingdom, Italy and Belgium. Frequent co-authors include Chris Taylor, Ariane L. Herrick, Andrea Murray, Graham Dinsdale, Tonia Moore, Joanne Manning, Susan Astley, Sue Astley, Caroline Boggis and Vanessa Smith. Their work appears in journals such as Lara D. Veeken, Microvascular Research, Scientific Reports, Magnetic Resonance in Medicine and Lecture notes in computer science.

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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