https://attendee.gotowebinar.com/register/5639902435734420492
Abstract
Machine learning and biophysical modelling are very complementary approaches. The recent progress in computing power and available data makes it possible to develop accurate data-driven approaches for healthcare, while biophysical models offer a principled way to represent anatomy and physiology. In this talk, Maxime Sermesant will present research where they combine both methodologies in order to leverage their strengths. Different clinical applications in computational cardiology will be presented.
Short bio
Host: Maxime Sermesant
Permanent researcher at Inria.
Researcher in Medical Image Analysis and Heart Modelling
Maxime Sermesant is a permanent researcher at Inria, the French research institute on informatics and mathematics. His research work combines biophysical and statistical modeling with clinical data. This is by nature a very collaborative and multi-disciplinary work at the intersection of academic, clinical and industrial environments. His research interests include biomedical image processing, organ modelling and machine learning. The integration of these areas open up possibilities in clinical data analysis for diagnosis, and in pathology simulation for therapy planning. His main focus has been the application of patient-specific models of the heart to cardiac pathologies. He received his Diploma in General Engineering from Ecole Centrale Paris, France in 1999, his MSc from Ecole Normale Superieure de Cachan, France in 1999, and his PhD in Control, Signal and Image Processing from the University of Nice – Sophia Antipolis, France in 2003. From June 2003 to December 2005, he was a Research Fellow with the Cardiac MR Research Group, Guy’s Hospital, King’s College London, UK and since 2005, he is a Research Scientist at Inria. He obtained a chair of AI & Biophysics at the 3IA Côte d’Azur AI Institute, and created the Multimodal Data Science team at IHU Liryc in 2019.
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