C. Perrenot

859 citations
30 papers · 564 · h-index 13

Impact in

Papers in

C. Perrenot

28 papers receiving 552 citations

Peers

C. Perrenot
Comparison fields: 5 of 74
  • General Dentistry 27
  • Surgery 451
  • Health Informatics 9
  • Computer Vision and Pattern Recognition 109
  • Biomedical Engineering 234
Replace P Vávra with:
P Vávra Czechia
P Zonča Czechia
Asaki Hattori Japan
Waleed Althobaity Canada
Lucian Panait United States
Manuela Perez France
Brian Mullis United States
Karen E. Burtt United States
Marc Gibber United States
Stine Maya Dreier Sørensen Denmark
C. Perrenot relative to P Vávra Czechia P Vávra's profile →
Citations per field
00.5×3.4×
P Vávra · 1×
Citations per year

Countries citing papers authored by C. Perrenot

Since Specialization
Citations

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

Fields of papers citing papers by C. Perrenot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014136
2 2012132
3 201732
4 201330
5 201426
6 201424
7 201619
8 201318
9 202017
10 201916
11 201614
12 201513
13 201712
14 202012
15 201611
16 201310
17 20199
18 20148
19 20206
20 20233

About C. Perrenot

C. Perrenot is a scholar working on Surgery, Biomedical Engineering, Computer Vision and Pattern Recognition, Physiology and Oncology, having authored 30 papers that have together received 564 indexed citations. Recurring topics across this work include Surgical Simulation and Training (22 papers), Anatomy and Medical Technology (13 papers), Simulation-Based Education in Healthcare (7 papers), Augmented Reality Applications (7 papers), Colorectal Cancer Surgical Treatments (4 papers), Hernia repair and management (3 papers), Innovations in Medical Education (3 papers) and Pancreatic and Hepatic Oncology Research (3 papers). The work is most often cited by research in General Dentistry (27 citations), Surgery (451 citations), Health Informatics (9 citations), Computer Vision and Pattern Recognition (109 citations) and Biomedical Engineering (234 citations). C. Perrenot has collaborated with scholars based in France, China and United States. Frequent co-authors include Jacques Hubert, Manuela Perez, Jacques Felblinger, Kun Yang, Song Xu, Nguyen Tran, Laurent Bresler, Laurent Brunaud, Nicolas Hubert and Adeline Germain. Their work appears in journals such as Surgical Endoscopy, Plastic & Reconstructive Surgery, Diseases of the Colon & Rectum, Updates in Surgery and Colorectal Disease.

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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