Dr. Ben Ayed received the PhD degree (with the highest honor) in computer vision from the INRS-EMT, Montreal in 2007. He is currently an Associate Professor at the ETS, University of Quebec. Before joining the ETS, he worked for 8 years as a research scientist at GE Healthcare, London, ON. He also holds an adjunct professor appointment at Western University (since 2012). Ismail's research interests include computer vision, optimization, machine learning and their potential applications in medical image analysis. He co-authored a book, over eighty peer-reviewed publications, mostly published in the top venues in these subject areas, and six US patents.

Pierre-Marc Jodoin is from the University of Sherbrooke, Canada where he works as a full professor since 2007. He specializes in the development of novel techniques for machine learning and deep learning applied to computer vision and medical imaging. He mostly works in video analytics and brain and cardiac image analytics. He is the co-director of the Sherbrooke AI platform and co-founder of the medical imaging company called "Imeka.ca" which specializes in MRI brain image analytics. web site: http://info.usherbrooke.ca/pmjodoin/

Herve Lombaert is Associate Professor of Computer Engineering at ETS Montreal and holds a Canada Research Chair in Shape Analysis in Medical Imaging. His research interests focus in Statistics on Shapes, Data & Medical Images, using graph analysis with applications in brain and cardiac imaging. He had the chance to work in multiple centers, including Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), Inria Sophia-Antipolis (France), McGill University (Canada), and Polytechnique Montreal (Canada). He is also a recipient of the François Erbsmann Prize, a top prize in Medical Image Analysis.

Prof. Desrosiers obtained a Ph.D. in Applied Mathematics from Polytechnique Montreal in 2008, and was a postdoctoral researcher at the University of Minnesota with prof George Karypis. In 2009, he joined École de technologie supérieure (ÉTS) as professor in the Departement of Software and IT Engineering. He is codirector of the Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA) and a member of the REPARTI research network. He has over 100 publications in the fields of machine learning, image processing, computer vision and medical imaging, and has served on the scientific committee of several important conferences in these fields.

Jose Dolz is Associate Professor in the Department of Software and IT Engineering at the ETS Montreal. Prior to be appointed Assistant Professor, he was a post-doctoral fellow at the ETS Montreal. Dr. Dolz obtained his B.Sc and M.Sc in the Polytechnic University of Valencia, Spain, and his Ph.D. at the University of Lille 2, France, in 2016. Dr. Dolz was recipient of a Marie-Curie FP7 Fellowship (2013-2016) to pursue his doctoral studies. His current research focuses on deep learning, medical imaging, optimization and learning strategies with limited supervision. He authored over 50 fully peer-reviewed papers, many of which published in the top venues in medical imaging (MedIA/TMI/MICCAI/IPMI), vision (CVPR) and learning (NeurIPS, ICML).

Dr.Havaei is a research scientist at Imagia. He focuses on developing machine learning methods applicable to healthcare. His research interests include representation learning, meta-learning and uncertainty. From 2016 to 2018 he was a member of Montreal Institute for Learning Algorithms (MILA) as a postdoctoral fellow under the supervision of Aaron Courville. Prior to that, he obtained my PhD at the University of Sherbrooke under the supervision of Hugo Larochelle and Pierre-Marc Jodoin. The main focus of his PhD was developing deep learning models applied to medical images for brain tumor segmentation.

Hassan Rivaz is an Associate Professor and a Concordia University Research Chair at the ECE Department with a joint appointment at the PERFORM Centre. He is also the Founding Director of the IMPACT Lab: IMage Processing And Characterization of Tissue. Dr. Rivaz is an Associate Editor of IEEE Trans. Medical Imaging (TMI, 2017-2022) and IEEE Trans. Ultrasonics, Ferroelectrics and Frequency Control (TUFFC, since 2018). He is a member of the Organizing Committee of IEEE EMBC 2020 in Montreal, IEEE ISBI 2021 in Nice, and IEEE IUS 2023 in Montreal. He has been an Area Chair of MICCAI for 4 years since 2017. He co-organized tutorials on advances in ultrasound imaging at IEEE ISBI 2019 and IEEE ISBI 2018. He also co-organized the CURIOUS 2018 and CURIOUS 2019 Challenges on registration of ultrasound and MRI in neurosurgery, and the CereVis 2018 Workshop on Cerebral Data Visualization, all in conjunction with MICCAI.

Dr. Taseen is a physician in respirology Ryeyan who earned a Master’s degree in computer science as part of his training as a clinical researcher at the Université de Sherbrooke. He is interested in clinical aid tools that make use of data contained in electronic medical records. He believes such tools are essential for the future of precision medicine.

The areas of research that captivate Ryeyan Taseen include precision medicine, clinical prediction models, clinical aid tools, and quality assessment of medical procedures.

Michaël Sdika is a full member of the CREATIS lab in Lyon, France. His current research field focuses on the development of new analysis method based on deep learning for medical data. His main contributions are centered around image registration, atlas based segmentation, structure localization and machine learning for MR image of the nervous central system.

Dr. Thomas Grenier is Associate Professor at INSA Lyon Electrical Engineering department and at the CREATIS lab in Lyon, France.

My research focuses on longitudinal analysis of medical data to study evolution as Multiple Sclerosis lesions, functional activity (muscle and hydrocephaly). Most of these studies involve a segmentation task and dedicated pre and post processing steps. Clustering (spatio-temporal mean-shift), semi-supervised (multi-atlas with machine learning) or fully supervised (DNN) schemes are used to solve such problems considering their specific constraints.

The research interests of professor Kadoury pertain to the development of novel methods and systems in medical imaging related to a broad range of clinical applications, including neurology, cardiovascular interventions, orthopaedics and interventional oncology. These new technologies in image registration (temporal, mono and multimodality), segmentation, organ atlas conception, statistical shape modeling, classification, and minimally invasive treatments are validated through clinical trials, both with adult and paediatric populations.