Simon Francis

597 citations
9 papers · 454 · h-index 4

Impact in

Papers in

Simon Francis

8 papers receiving 433 citations

Peers

Simon Francis
Comparison fields: 5 of 64
  • Neurology 126
  • Computer Vision and Pattern Recognition 199
  • Pathology and Forensic Medicine 117
  • Radiology, Nuclear Medicine and Imaging 116
  • Biophysics 24
Replace Rasoul Khayati with:
Rasoul Khayati Iran
Ivan Coronado United States
Nagesh K. Subbanna Canada
Sandra González-Villà Spain
Eloy Roura Spain
Liyu Wei China
Sheeba J. Sujit United States
Ezequiel Geremia France
Loredana Storelli Italy
Sushmita Datta United States
Simon Francis relative to Rasoul Khayati Iran Rasoul Khayati's profile →
Citations per field
00.5×1.5×2.4×
Rasoul Khayati · 1×
Citations per year

Countries citing papers authored by Simon Francis

Since Specialization
Citations

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

Fields of papers citing papers by Simon Francis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 12 scholars most cited alongside Simon Francis, 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 Simon Francis Line = papers co-authored together Simon Francis links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 2012275
2 2010134
3 200629
4 20106
5 20203
6 20173
7 19723
8 20091
9 20090

About Simon Francis

Simon Francis is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology, Artificial Intelligence, Pathology and Forensic Medicine and Strategy and Management, having authored 9 papers that have together received 454 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (3 papers), Gene expression and cancer classification (2 papers), Digital Imaging for Blood Diseases (2 papers), Bayesian Methods and Mixture Models (2 papers), State Capitalism and Financial Governance (1 paper), International Arbitration and Investment Law (1 paper), Natural Resources and Economic Development (1 paper) and Integrated Circuits and Semiconductor Failure Analysis (1 paper). The work is most often cited by research in Neurology (126 citations), Computer Vision and Pattern Recognition (199 citations), Pathology and Forensic Medicine (117 citations), Radiology, Nuclear Medicine and Imaging (116 citations) and Biophysics (24 citations). Simon Francis has collaborated with scholars based in Canada, Sweden and United Kingdom. Frequent co-authors include D. Louis Collins, Douglas L. Arnold, Daniel García-Lorenzo, Sridar Narayanan, Tal Arbel, Mohak Shah, Yiming Xiao, Nagesh K. Subbanna, Rola Harmouche and Phil Legg. Their work appears in journals such as Medical Image Analysis, The Russian Review, NeuroImage, Asian Affairs 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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