Michel Dojat

10.4k total citations
108 papers, 2.2k citations indexed

About

Michel Dojat is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michel Dojat has authored 108 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Cognitive Neuroscience, 24 papers in Artificial Intelligence and 21 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michel Dojat's work include Visual perception and processing mechanisms (21 papers), Medical Image Segmentation Techniques (16 papers) and Neural dynamics and brain function (12 papers). Michel Dojat is often cited by papers focused on Visual perception and processing mechanisms (21 papers), Medical Image Segmentation Techniques (16 papers) and Neural dynamics and brain function (12 papers). Michel Dojat collaborates with scholars based in France, Switzerland and United Kingdom. Michel Dojat's co-authors include Catherine Garbay, Florence Forbes, A. Harf, Laurent Brochard, F Lemaire, Cécile Bordier, Laurent Brochard, Chantal Delon‐Martin, Dominique Touchard and Jan Warnking and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and NeuroImage.

In The Last Decade

Michel Dojat

99 papers receiving 2.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michel Dojat France 27 603 586 422 347 312 108 2.2k
Michel Toussaint France 27 909 1.5× 552 0.9× 145 0.3× 170 0.5× 36 0.1× 85 2.5k
Shamim Nemati United States 31 456 0.8× 610 1.0× 89 0.2× 1.3k 3.7× 119 0.4× 131 3.9k
Warren D. Smith United States 27 140 0.2× 268 0.5× 213 0.5× 480 1.4× 241 0.8× 123 3.3k
Chandan Karmakar Australia 31 258 0.4× 881 1.5× 25 0.1× 319 0.9× 173 0.6× 174 3.8k
Hung‐Wen Chiu Taiwan 25 168 0.3× 170 0.3× 59 0.1× 227 0.7× 42 0.1× 108 1.7k
Robert J. Sclabassi United States 37 323 0.5× 1.2k 2.0× 67 0.2× 262 0.8× 346 1.1× 278 4.7k
Mark van Gils Finland 31 74 0.1× 376 0.6× 119 0.3× 194 0.6× 251 0.8× 135 3.0k
David Snyder United States 27 79 0.1× 628 1.1× 112 0.3× 3.0k 8.8× 195 0.6× 54 4.9k
Stephen Bonner United Kingdom 17 150 0.2× 80 0.1× 198 0.5× 161 0.5× 101 0.3× 54 1.1k
Chris Wells United States 21 110 0.2× 141 0.2× 123 0.3× 329 0.9× 51 0.2× 81 3.9k

Countries citing papers authored by Michel Dojat

Since Specialization
Citations

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

Fields of papers citing papers by Michel Dojat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michel Dojat

This figure shows the co-authorship network connecting the top 25 collaborators of Michel Dojat. A scholar is included among the top collaborators of Michel Dojat based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michel Dojat. Michel Dojat is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Winter, Sophie L., Josh Moore, Adriana Tavares, et al.. (2025). A global effort toward standards for data sharing in biomedical imaging. EMBO Reports. 27(1). 10–14.
2.
3.
Forbes, Florence, et al.. (2024). Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis. Artificial Intelligence in Medicine. 150. 102830–102830. 63 indexed citations
4.
Barbier, Emmanuel, et al.. (2023). ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization. Medical Image Analysis. 88. 102799–102799. 25 indexed citations
5.
Dojat, Michel, et al.. (2023). Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients. arXiv (Cornell University). 12. 1–5. 1 indexed citations
6.
Kauffmann, Louise, et al.. (2021). Effective connectivity in subcortical visual structures in de novo Patients with Parkinson’s Disease. NeuroImage Clinical. 33. 102906–102906. 8 indexed citations
7.
Dojat, Michel, et al.. (2020). Multivariate pattern analysis of fMRI data for imaginary and real colours in grapheme–colour synaesthesia. European Journal of Neuroscience. 52(5). 3434–3456. 1 indexed citations
8.
Forbes, Florence, et al.. (2019). No Structural Differences Are Revealed by VBM in ‘De Novo’ Parkinsonian Patients. Studies in health technology and informatics. 264. 268–272. 3 indexed citations
9.
Richard, Jean-Christophe M., et al.. (2014). Feasibility and reliability of an automated controller of inspired oxygen concentration during mechanical ventilation. Critical Care. 18(1). R35–R35. 15 indexed citations
10.
Bordier, Cécile, Michel Dojat, & Pierre Lafaye de Micheaux. (2011). Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package. SHILAP Revista de lepidopterología. 6 indexed citations
11.
Jaillard, Assia, et al.. (2007). Segmentation of Magnetic Resonance Brain Images Using Edge and Region Cooperation Characterization of Stroke Lesions.. The International Arab Journal of Information Technology. 4. 281–288. 5 indexed citations
12.
Gibaud, Bernard, et al.. (2006). OntoNeuroBase: a multi-layered application ontology in neuroimaging. HAL (Le Centre pour la Communication Scientifique Directe). 7 indexed citations
13.
Mancebo, Jordi, Philippe Jolliet, Jean Roeseler, et al.. (2006). A Multicenter Randomized Trial of Computer-driven Protocolized Weaning from Mechanical Ventilation. American Journal of Respiratory and Critical Care Medicine. 174(8). 894–900. 260 indexed citations
14.
Warnking, Jan, Michel Dojat, Chantal Delon‐Martin, Nathalie Richard, & Christoph Segebarth. (2004). Délinéation des aires visuelles rétinotopiques chez le sujet individuel à l'aide de l'IRM fonctionnelle. Comptes Rendus Chimie. 7(3-4). 207–212.
15.
Dojat, Michel, Elpida Keravnou, & Pedro Barahona. (2003). Artificial Intelligence in Medicine: 9th Conference on Artificial Intelligence in Medicine in Europe, Aime 2003, Protaras, Cyprus, October 18-22, 2003, Proceedings (Lecture Notes in Computer Science, 2780.). Springer eBooks. 3 indexed citations
16.
Richard, Nathalie, Michel Dojat, & Catherine Garbay. (2002). Situated cooperative agents: a powerful paradigm for MRI brain scans segmentation. European Conference on Artificial Intelligence. 33–37. 1 indexed citations
17.
Dojat, Michel, A. Harf, Dominique Touchard, F Lemaire, & Laurent Brochard. (2000). Clinical Evaluation of a Computer-controlled Pressure Support Mode. American Journal of Respiratory and Critical Care Medicine. 161(4). 1161–1166. 103 indexed citations
18.
Dojat, Michel, et al.. (1996). Evaluation of a Knowledge-Based System Providing Ventilatory Management and Decision for Extubation. American Journal of Respiratory and Critical Care Medicine. 153(3). 997–1004. 90 indexed citations
19.
Dojat, Michel & F. Pachet. (1992). Representation of a medical expertise using the Smalltalk environment: putting a prototype to work. 379–389. 2 indexed citations
20.
Chittaro, Luca, et al.. (1970). Explicit Representation Of Temporal Aspects In AMedical Monitoring System Using CEC. WIT transactions on information and communication technologies. 10. 1 indexed citations

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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