Author

Christian Desrosiers

École de Technologie SupérieureORCID 0000-0002-9162-9650Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:dimensionality reduction· 1 Approach:statistical model· 1

Frequent Co-Authors

Abdulah FawaziD· 1 shared Amir AlansaryiD· 1 shared Anthony David EdwardsiD· 1 shared Antoine ThébergeiD· 1 shared Bonnie AlexanderiD· 1 shared Cher BassiD· 1 shared Chris L. AdamsoniD· 1 shared Daniel RueckertiD· 1 shared

Activity by Year

2018-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperNeuroscience

Abdulah FawazORCID

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3 papers

Recent·Most cited

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

Abdulah Fawaz, Logan Z. J. Williams, Amir Alansary, Cher Bass, Karthik Kuduva Gopinath, Mariana da Silva, Simon Dahan, Chris L. Adamson, Bonnie Alexander, Deanne Kim Thompson, Gareth Ball, Christian Desrosiers, Hervé Lombaert, Daniel Rueckert, Anthony David Edwards, Emma C. Robinson

This paper benchmarks geometric and traditional deep learning models for cortical segmentation and neurodevelopmental phenotype prediction using neonatal cortical surface data.

bioRxiv · Dec 1, 2021 · PreprintNeuroscience

Track-To-Learn: A general framework for tractography with deep reinforcement learning

Antoine Théberge, Christian Desrosiers, Maxime Descoteaux, Pierre‐Marc Jodoin

Track-To-Learn uses deep reinforcement learning to improve diffusion MRI tractography without requiring labeled data, achieving better generalization than prior methods.

Medical Image Analysis · May 3, 2021Peer-reviewedNeuroscience

Multi-modal brain fingerprinting: a manifold approximation based framework

Kuldeep Kumar, Matthew Toews, Laurent Chauvin, Olivier Colliot, Christian Desrosiers

This paper introduces a manifold approximation framework for generating brain fingerprints from multi-modal data, showing that multi-modal fingerprints are more discriminative and genetically informative than single-modal ones.

NeuroImage · Aug 9, 2018Peer-reviewedNeuroscience