Simon Francis

619 citations
8 papers · 398 · h-index 4

Impact in

Papers in

Simon Francis

7 papers receiving 380 citations

Peers

Simon Francis
Comparison fields: 5 of 64
  • Neurology 128
  • Computer Vision and Pattern Recognition 191
  • Radiology, Nuclear Medicine and Imaging 149
  • Pathology and Forensic Medicine 102
  • Biophysics 28
Replace Rasoul Khayati with:
Rasoul Khayati Iran
Nagesh K. Subbanna Canada
Ivan Coronado United States
Sandra González-Villà Spain
Eloy Roura Spain
Ezequiel Geremia France
Liyu Wei China
Sheeba J. Sujit United States
Ayelet Akselrod-Ballin Israel
Jacob C. Reinhold United States
Simon Francis relative to Rasoul Khayati Iran Rasoul Khayati's profile →
Citations per field
00.5×1.5×2.0×
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 13 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

8 of 8 papers shown
#Work
1 2012246
2 2010116
3 200628
4 20243
5 20202
6 19722
7 20091
8 20090

About Simon Francis

Simon Francis is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology, Pathology and Forensic Medicine, Strategy and Management and Pulmonary and Respiratory Medicine, having authored 8 papers that have together received 398 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (2 papers), Medical Image Segmentation Techniques (2 papers), Prostate Cancer Diagnosis and Treatment (1 paper), Image Processing Techniques and Applications (1 paper), Natural Resources and Economic Development (1 paper), Multiple Sclerosis Research Studies (1 paper), State Capitalism and Financial Governance (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Neurology (128 citations), Computer Vision and Pattern Recognition (191 citations), Radiology, Nuclear Medicine and Imaging (149 citations), Pathology and Forensic Medicine (102 citations) and Biophysics (28 citations). Simon Francis has collaborated with scholars based in Canada, Sweden and India. Frequent co-authors include D. Louis Collins, Douglas L. Arnold, Sridar Narayanan, Daniel García-Lorenzo, Tal Arbel, Yiming Xiao, Nagesh K. Subbanna, Mohak Shah, Rola Harmouche and Mustafa Radha. Their work appears in journals such as Medical Image Analysis, The Russian Review, NeuroImage, Asian Affairs and Remote Sensing in Earth Systems Sciences.

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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