[G-Stats Seminar] Nicolas Duchateau

Nicolas Duchateau

CREATIS LAB – University of Lyon 1

19/11/2021 – h. 10.30 a.m.

Bio: Nicolas is currently an Associate Professor (Maître de Conférences) at the Université Lyon 1 and the CREATIS lab in Lyon, France. His research focuses on the characterization of diseases from medical imaging populations. In particular, he develops new statistical and computational approaches to represent the cardiac function, and better understand disease apparition and evolution.

Cardiac function analysis with representation learning

Abstract: The cardiac function can be studied from many points of view, ranging from the original images, up to physiologically-relevant descriptors of its shape or deformation along time. However, clinical practice severely truncates these data by relying on arbitrary thresholds and scalar measurements. Several techniques from representation learning such as manifold learning or variational auto-encoders allow estimating a latent space that encodes more complex descriptors of the cardiac function, and is statistically relevant to compare individuals or subgroups. Challenges consist in handling high-dimensional descriptors that originate from a non-linear (unknown) space, of heterogeneous types and with potential interactions between descriptors. In this talk, I will provide an overview of some representation learning techniques relevant for analyzing the cardiac function, with specific focus on studies involving multiple acquisitions or descriptors, and the need for representations that can be interpreted and trusted by medical doctors.

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