Portrait of Julien Cohen-Adad

Julien Cohen-Adad

Associate Academic Member
Associate Professor, Polytechnique Montréal, Electrical Engineering Department
Adjunct Professor, Université de Montréal, Department of Neuroscience
Research Topics
Medical Machine Learning

Biography

Julien Cohen-Adad is a professor at Polytechnique Montréal and the associate director of the Neuroimaging Functional Unit at Université de Montréal. He is also the Canada Research Chair in Quantitative Magnetic Resonance Imaging.

His research focuses on advancing neuroimaging methods with the help of AI. Some examples of projects are:

- Multi-modal training for medical imaging tasks (segmentation of pathologies, diagnosis, etc.)

- Adding prior from MRI physics to improve model generalization

- Incorporating uncertainty measures to deal with inter-rater variability

- Continuous learning strategies when data sharing is restricted

- Bringing AI methods into clinical radiology routine via user-friendly software solutions

Cohen-Adad also leads multiple open-source software projects that are benefiting the research and clinical community (see neuro.polymtl.ca/software.html). In short, he loves MRI with strong magnets, neuroimaging, programming and open science!

Current Students

PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Research Intern - Polytechnique Montréal
PhD - Polytechnique Montréal
Research Intern - Polytechnique Montréal
Master's Research - Polytechnique Montréal

Publications

2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
Nilser J. Laines Medina
Charley Gros
Virginie Callot
Arnaud Le Troter
The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurologica… (see more)l disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired. While automated methods for WM/GM segmentation are now largely available, these techniques, developed for conventional systems (3T or lower) do not necessarily perform well on 7T MRI data, which feature finer details, contrasts, but also different artifacts or signal dropout. The primary goal of this study is thus to propose a new deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images. The second objective is to highlight the relevance of implementing a specific data augmentation (DA) strategy, in particular to generate a generic model that could be used for multi-center studies at 7T.
Quantitative MRI of the spinal cord: Reproducibility and normative values across 40 sites
Eva Alonso‐Ortiz
Charley Gros
Alexandru Foias
Mihael Abramovic
Christoph Arneitz
Nicole Atcheson
Laura Barlow
Robert Barry
Markus Barth
Marco Battiston
Christian Büchel
Matthew D. Budde
Virginie Callot
Benjamin De Leener
Maxime Descoteaux
Paulo Loureiro de Sousa
Marek Dostál
Julien Doyon
Adam Dvorak
Falk Eippert … (see 60 more)
Karla R. Epperson
Jürgen Finsterbusch
Issei Fukunaga
Claudia A. M. Gandini Wheeler‐Kingshott
Giancarlo Germani
Guillaume Gilbert
Francesco Grussu
Akifumi Hagiwara
Pierre‐Gilles Henry
Thomas Horak
Masaaki Hori
James M. Joers
Kouhei Kamiya
Haleh Karbasforoushan
Ali Khatibi
Joo-Won Kim
Nawal Kinany
Hagen H. Kitzler
Shannon Kolind
Joe Yazhuo Kong
Shannon Kolind
Paul Kuntke
Nyoman D. Kurniawan
Sławomir Kuśmia
René Labounek
Maria Marcella Laganà
Corree Laule
Christine Law
Christophe Lenglet
Tobias Leutritz
Yaou Liu
Sara Llufriú
Sean Mackey
Eloy Jiménez Martínez
Igor Nestrašil
Nico Papinutto
Daniel S. Papp
Deborah Pareto
Todd B. Parrish
Anna Pichiecchio
À. Rovira Cañellas
Marc J. Ruitenberg
Rebecca S. Samson
G. Savini
Maryam Seif
Alan C. Seifert
Alex K. Smith
Zachary A. Smith
Elisabeth Solana
Yuichi Suzuki
George Tackley
Alexandra Tinnermann
Marios Yiannakas
Kenneth A. Weber
Nikolaus Weiskopf
Richard G. Wise
Patrik O. Wyss
Junqian Xu
Julien Cohen‐Adad