Laboratoire d’innovation

Activité de partage et de réseautage entre la communauté scientifique de la science des données et celle de la santé

Le 29 avril 2019, de 16h à 17h30

Auditorium du MILA, 6650, rue Saint-Urbain, Montréal, 1^er étage

Pour inscription : https://www.meetup.com/AIForHealth/

Conférenciers

Julien Cohen-Adad

Polytechnique de Montréal

Connecting MRI physics and A.I. to advance neuroimaging

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 sensitize the DL community to unmet needs in MRI analysis, and explore possible ways to leverage MRI physics to advance impactful DL applications in the medical domain.

Mathieu Lavallée-Adam

Département de biochimie, microbiologie et immunologie

Faculté de médecine, Université d’Ottawa

Titre de la conférence à venir

Intérêts de recherche: Dr. Lavallée-Adam’s research yielded, among other things, software packages for the discovery and confidence assessment of protein-protein interactions and the analysis of the networks formed by these interactions. He also developed algorithms for the analysis of quantitative proteomics data obtained in different experimental conditions and the identification of intact proteins and large polypeptides using mass spectrometry. Finally, his lab’s most recent work aims to develop computational tools for the prediction of drug targets and off-target effects using large-scale mass spectrometry.

Au plaisir de vous compter parmi nous!

Margarida Carvalho

Sébastien Lemieux

Joseph Paul Cohen

Co-organisateurs de la communauté d’échanges