Mark Halling‐Brown

5.7k citations
41 papers · 469 · h-index 12

Impact in

Papers in

Mark Halling‐Brown

40 papers receiving 457 citations

Peers

Mark Halling‐Brown
Comparison fields: 5 of 79
  • Radiology, Nuclear Medicine and Imaging 100
  • Pulmonary and Respiratory Medicine 119
  • Computational Theory and Mathematics 64
  • Health Informatics 4
  • Molecular Biology 194
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Lujia Chen United States
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Taylor E. Arnoff United States
Muhammad Arif China
Te-Cheng Hsu Taiwan
Carly A. Bridge United States
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Citations per field
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Citations per year

Countries citing papers authored by Mark Halling‐Brown

Since Specialization
Citations

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

Fields of papers citing papers by Mark Halling‐Brown

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Halling‐Brown. 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 Mark Halling‐Brown. The network helps show where Mark Halling‐Brown may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201293
2 201167
3 201635
4 200829
5 201422
6 201516
7 200716
8 201414
9 200814
10 200813
11 200611
12 200911
13 201811
14 201411
15 201510
16 201410
17 20089
18 20148
19 20097
20 20047

About Mark Halling‐Brown

Mark Halling‐Brown is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Molecular Biology and Oncology, having authored 41 papers that have together received 469 indexed citations. Recurring topics across this work include AI in cancer detection (19 papers), Digital Radiography and Breast Imaging (15 papers), Radiomics and Machine Learning in Medical Imaging (12 papers), Medical Imaging Techniques and Applications (6 papers), vaccines and immunoinformatics approaches (6 papers), T-cell and B-cell Immunology (3 papers), Monoclonal and Polyclonal Antibodies Research (3 papers) and Colorectal Cancer Screening and Detection (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (100 citations), Pulmonary and Respiratory Medicine (119 citations), Computational Theory and Mathematics (64 citations), Health Informatics (4 citations) and Molecular Biology (194 citations). Mark Halling‐Brown has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Mishal Patel, Bissan Al‐Lazikani, Kenneth C. Young, Joseph E Tym, Paul Workman, David S. Moss, Alistair Mackenzie, Krishna C. Bulusu, Lucy M. Warren and David R. Dance. Their work appears in journals such as Nucleic Acids Research, Physica Medica, Trends in Immunology, Journal of Molecular Graphics and Modelling and International Journal of Immunogenetics.

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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