Search & Find
DiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporamaDiaporama
Home
ICube Laboratory   >   Events : ICube-MSII seminar: Connecting MRI physics and A.I. to advance neuroimaging

ICube-MSII seminar: Connecting MRI physics and A.I. to advance neuroimaging

December 13, 2018
14:00
Illkirch - Pole API - amphi A301

Julien Cohen-Adad, PhD (Ecole Polytechnique, University of Montreal, Canada) will give a talk, Thursday 13th december 2018 at 2:00 PM in room A301 of the pole API building in Illkirch.

Title: Connecting MRI physics and A.I. to advance neuroimaging

Abstract: Magnetic Resonance Imaging (MRI) can have multiple flavours: T1, T2, proton density, fMRI, diffusion MRI, etc. These so-called "Quantitative MRI" techniques are useful for monitoring pathologies such as multiple sclerosis and Alzheimer's disease. However, quantitative MRI data require complex analysis pipelines that are often executed manually and hence suffer from poor reproducibility. Deep learning (DL) appears to be an ideal candidate to help automatize certain analysis tasks. Unfortunately, while dozens of DL papers applied to medical imaging are published every year, most methods have been validated in well-curated single-center datasets only. In the rare case where the code is publicly available, the algorithm usually fails when applied to other centers (a.k.a. Real life data!). This happens because images across different centers have slightly different features than those used to train the algorithm (contrast, resolution, etc.), combined with the fact that low amount of data and manual labels are available. Recent DL techniques such as domain adaptation have tackled this issue. However, these techniques are not well adapted to our situation because in MRI, image features not only varies between centers, but also across a large number of acquisition parameters (e.g., repetition time, flip angle). The purpose of this presentation is to explore possible ways to leverage MRI physics to advance impactful DL applications in the medical domain.

Bio: Dr. Cohen-Adad is an Associate Professor at Polytechnique Montreal, Adjunct Professor in the Department of Neurosciences at University of Montreal, Associate Director of the Neuroimaging Functional Unit at the University of Montreal, and Canada Research Chair in Quantitative Magnetic Resonance Imaging. His research focuses on advancing quantitative MRI methods for characterizing pathologies in the central nervous system.

À la une

Internships subjects of the 15 teams are available in our "Job opportunities" tab or directly by...

RSS Feeds

Flux RSS