Michaël Sdika

1.5k total citations
45 papers, 946 citations indexed

About

Michaël Sdika is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Michaël Sdika has authored 45 papers receiving a total of 946 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Computer Vision and Pattern Recognition and 13 papers in Biomedical Engineering. Recurrent topics in Michaël Sdika's work include Advanced MRI Techniques and Applications (14 papers), Optical Imaging and Spectroscopy Techniques (11 papers) and Medical Image Segmentation Techniques (9 papers). Michaël Sdika is often cited by papers focused on Advanced MRI Techniques and Applications (14 papers), Optical Imaging and Spectroscopy Techniques (11 papers) and Medical Image Segmentation Techniques (9 papers). Michaël Sdika collaborates with scholars based in France, United States and Canada. Michaël Sdika's co-authors include Daniel Pelletier, H. Ratiney, D. Graveron‐Demilly, D. van Ormondt, S. Cavassila, Arnaud Le Troter, Bill Triggs, Virginie Callot, Manuel Taso and Julien Cohen‐Adad and has published in prestigious journals such as NeuroImage, Brain and IEEE Transactions on Signal Processing.

In The Last Decade

Michaël Sdika

41 papers receiving 925 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Michaël Sdika 517 195 152 126 92 45 946
Joon Yul Choi 736 1.4× 190 1.0× 107 0.7× 67 0.5× 52 0.6× 68 1.2k
Refaat E. Gabr 422 0.8× 95 0.5× 86 0.6× 161 1.3× 48 0.5× 54 809
Dong‐Hyun Kim 480 0.9× 113 0.6× 167 1.1× 27 0.2× 120 1.3× 81 1.2k
Ferenc A. Jolesz 554 1.1× 153 0.8× 105 0.7× 45 0.4× 29 0.3× 12 824
Chuan Huang 988 1.9× 82 0.4× 232 1.5× 71 0.6× 168 1.8× 113 1.8k
Dosik Hwang 1.1k 2.2× 247 1.3× 453 3.0× 112 0.9× 28 0.3× 87 1.8k
James G. Malcolm 629 1.2× 217 1.1× 115 0.8× 80 0.6× 79 0.9× 65 1.7k
E. Brian Welch 1.4k 2.7× 60 0.3× 146 1.0× 90 0.7× 120 1.3× 81 2.0k
Bruno Alfano 759 1.5× 175 0.9× 90 0.6× 448 3.6× 203 2.2× 63 1.8k
Jean‐Paul Armspach 308 0.6× 262 1.3× 70 0.5× 261 2.1× 71 0.8× 47 933

Countries citing papers authored by Michaël Sdika

Since Specialization
Citations

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

Fields of papers citing papers by Michaël Sdika

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michaël Sdika

This figure shows the co-authorship network connecting the top 25 collaborators of Michaël Sdika. A scholar is included among the top collaborators of Michaël Sdika 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 Michaël Sdika. Michaël Sdika 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.
Chopinet, Sophie, O. Lopez, Sophie Brustlein, et al.. (2024). Comparing Different Methods for the Diagnosis of Liver Steatosis: What Are the Best Diagnostic Tools?. Diagnostics. 14(20). 2292–2292.
2.
Grenier, Thomas, et al.. (2024). An unexpected confounder: how brain shape can be used to classify MRI scans ?. SPIRE - Sciences Po Institutional REpository.
3.
Sdika, Michaël, et al.. (2024). The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging. SPIRE - Sciences Po Institutional REpository. 75–84. 2 indexed citations
4.
Grenier, Thomas, et al.. (2024). Explainable monotonic networks and constrained learning for interpretable classification and weakly supervised anomaly detection. Pattern Recognition. 160. 111186–111186. 1 indexed citations
5.
6.
Grenier, Thomas, et al.. (2023). A Weakly Supervised Gradient Attribution Constraint for Interpretable Classification and Anomaly Detection. IEEE Transactions on Medical Imaging. 42(11). 3336–3347. 9 indexed citations
7.
Ratiney, H., et al.. (2023). Evaluation of deep learning models for quality control of MR spectra. Frontiers in Neuroscience. 17. 1 indexed citations
8.
Reeth, Éric Van, Michaël Sdika, F. Schneider, et al.. (2023). Intraoperative identification of functional brain areas with RGB imaging using statistical parametric mapping: Simulation and clinical studies. NeuroImage. 278. 120286–120286. 3 indexed citations
9.
Grenier, Thomas, et al.. (2021). A More Interpretable Classifier For Multiple Sclerosis. HAL (Le Centre pour la Communication Scientifique Directe). 1062–1066. 6 indexed citations
10.
Martin, Matthieu, et al.. (2021). Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN. Computers in Biology and Medicine. 131. 104268–104268. 10 indexed citations
11.
Leporq, Benjamin, Amine Bouhamama, F. Pilleul, et al.. (2020). MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study. Cancer Imaging. 20(1). 78–78. 26 indexed citations
12.
Grenier, Thomas, et al.. (2019). Automatic myocardial ischemic lesion detection on magnetic resonance perfusion weighted imaging prior perfusion quantification: A pre-modeling strategy. Computers in Biology and Medicine. 110. 108–119. 2 indexed citations
13.
Sdika, Michaël, et al.. (2019). Repetitive motion compensation for real time intraoperative video processing. Medical Image Analysis. 53. 1–10. 7 indexed citations
14.
Sdika, Michaël, Anne Tonson, Yann Le Fur, Patrick J. Cozzone, & David Bendahan. (2015). Multi-atlas-based fully automatic segmentation of individual muscles in rat leg. Magnetic Resonance Materials in Physics Biology and Medicine. 29(2). 223–235. 3 indexed citations
15.
Sdika, Michaël. (2015). Enhancing atlas based segmentation with multiclass linear classifiers. Medical Physics. 42(12). 7169–7181. 3 indexed citations
16.
Fonov, Vladimir, Arnaud Le Troter, Manuel Taso, et al.. (2014). Framework for integrated MRI average of the spinal cord white and gray matter: The MNI–Poly–AMU template. NeuroImage. 102. 817–827. 81 indexed citations
17.
Gourraud, Pierre‐Antoine, Michaël Sdika, Pouya Khankhanian, et al.. (2013). A genome-wide association study of brain lesion distribution in multiple sclerosis. Brain. 136(4). 1012–1024. 38 indexed citations
18.
Taso, Manuel, Arnaud Le Troter, Michaël Sdika, et al.. (2013). Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. Magnetic Resonance Materials in Physics Biology and Medicine. 27(3). 257–267. 43 indexed citations
19.
Sdika, Michaël. (2008). A Fast Nonrigid Image Registration With Constraints on the Jacobian Using Large Scale Constrained Optimization. IEEE Transactions on Medical Imaging. 27(2). 271–281. 70 indexed citations
20.
Ratiney, H., Susan M. Noworolski, Michaël Sdika, et al.. (2007). Estimation of metabolite T 1 relaxation times using tissue specific analysis, signal averaging and bootstrapping from magnetic resonance spectroscopic imaging data. Magnetic Resonance Materials in Physics Biology and Medicine. 20(3). 143–155. 12 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Joon Yul Choi Breakdown of academic impact, for papers by Refaat E. Gabr Breakdown of academic impact, for papers by Dong‐Hyun Kim Breakdown of academic impact, for papers by Ferenc A. Jolesz Breakdown of academic impact, for papers by Chuan Huang Breakdown of academic impact, for papers by Dosik Hwang Breakdown of academic impact, for papers by James G. Malcolm Breakdown of academic impact, for papers by E. Brian Welch Breakdown of academic impact, for papers by Bruno Alfano Breakdown of academic impact, for papers by Jean‐Paul Armspach
Rankless by CCL
2026