Author

David Rousseau

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementORCID 0000-0002-7935-1609Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

David Rousseau1 paper

Biology Disciplines

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Bioinformatics· 2 Biophysics· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Approach:deep learning· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:microscopy & imaging analysis· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

Ahmed IdbaïhiD· 1 shared Alexis J. Chambost· 1 shared Ali Ahmad· 1 shared Aurélien GourrieriD· 1 shared Carole FrindeliD· 1 shared Caroline IsaaciD· 1 shared Charlotte RivièreiD· 1 shared David MeyronetiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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1 shared paperBioinformatics

Ahmed IdbaïhORCID

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3 papers

Recent·Most cited

Cellular porosity in dentin exhibits complex network characteristics with spatio-temporal fluctuations

Lucas Chatelain, Nicolas Tremblay, Elsa Vennat, Elisabeth Dursun, David Rousseau, Aurélien Gourrier

This study models dentin porosity as a spatial graph to analyze its network characteristics and shows that porosity exhibits complex network properties with significant spatial variation.

PLoS ONE · Jul 16, 2025Peer-reviewedBiophysics

Machine learning-based detection of label-free cancer stem-like cell fate

Alexis J. Chambost, Nabila Berabez, Olivier Cochet‐Escartin, François Ducray, Mathieu Gabut, Caroline Isaac, Sylvie Martel, Ahmed Idbaïh, David Rousseau, David Meyronet, Sylvain Monnier

This study develops a deep learning algorithm for analyzing label-free brightfield microscopy images to detect cancer stem-like cell fate, showing improved accuracy and speed compared to other methods.

Scientific Reports · Nov 9, 2022Peer-reviewedBioinformatics

Clearing spheroids for 3D fluorescent microscopy:combining safe and soft chemicals with deep convolutional neural network

Ali Ahmad, Saba Goodarzi, Carole Frindel, Gaëlle Recher, Charlotte Rivière, David Rousseau

The paper develops a method to quantify clearing efficiency in 3D cell spheroids using a deep convolutional neural network and local contrast metrics.

bioRxiv · Jan 31, 2021 · PreprintBioinformatics