Speakers

Speakers and contributors

Olivier Bernard - CREATIS laboratory, Lyon, France

Olivier Bernard received his Electrical Engineering degree and Ph.D. from the University of Lyon (INSA), France, in 2003 and 2006, respectively. He was a Postdoctoral Fellow with the Biomedical Imaging Group at the Federal Polytechnic Institute of Lausanne, EPFL, Switzerland in 2007. Currently, he is a Professor with the University of Lyon (INSA) and the CREATIS laboratory in France. He is also the head of the Myriad research team, which specializes in medical image analysis, simulation, and modeling. His current research interests focus on image analysis through deep learning techniques, with applications in cardiovascular imaging, blood flow imaging, and population representation. Prof. Bernard was also an Associate Editor of the IEEE Transactions on Image Processing.

Christian Desrosiers - École de Technologie Supérieure, Canada

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.

Presentation abstracts

• Generative, auto-encoders and adversarial methods for medical imaging  together with Olivier Bernard

Generative models are now well-established tools in medical imaging, successfully applied to dimensionality reduction, generative processes, and domain adaptation. In this talk, we will present the two most popular techniques and their variants: auto-encoders and GANs. For each case, we will provide a clear mathematical background, along with a large set of examples.

•  Basics of deep learning - Part I

In the first part of the tutorial, we start by introducing the basic element of neural networks, the neuron, and explain how it relates to linear regression and classification. We then present the logistic regression and Perceptron models for binary classification, describe their corresponding loss functions, and show how these models can be trained with a stochastic gradient descent algorithm. We end the first part of the tutorial by explaining how logistic regression and the Perceptron can be extended to multi-layer networks and multiclass classification tasks.

• Basics of deep learning - Part II

The second part of the tutorial first covers the fundamental principles of training neural networks, including backpropagation and mini-batch stochastic gradient descent. We then present the main activation functions for neural networks, describing their respective advantages and drawbacks. We finish the tutorial by introducing convolutional neural networks (CNN), presenting their key properties, and illustrating their use in different image-based applications.

Nicolas Duchateau - CREATIS laboratory, Lyon, France

Nicolas Duchateau is Associate Professor (Maître de Conférences) at the Université Lyon 1 and the CREATIS lab in Lyon, France. His research focuses on the statistical analysis of medical imaging data to better understand disease apparition and evolution, and to a certain extent computer-aided diagnosis. On the technical side, it mainly covers post-processing through statistical atlases and machine learning techniques. It also includes dedicated pre-processing and validation, among which the generation of synthetic databases. On the clinical/applicative side, it covers the study of cardiac function from heart failure populations, through routine imaging data and advanced 2D/3D shape, motion and deformation descriptors.

Nicolas Ducros - CREATIS Laboratory, Lyon, France

Nicolas Ducros has been an Associate Professor in the Electrical Engineering Department of Lyon University and with the Biomedical Imaging Laboratory CREATIS since 2014. His research interests include signal and image processing, and applied inverse problems with particular emphasis on single-pixel imaging and spectral computed tomography. His recent work focus on deep learning for image reconstruction and, in particular,  on network architectures that can be interpreted as conventional reconstruction methods. He is an Associated Member of the IEEE Bio Imaging and Signal Processing Technical Committee.

Presentation abstract

• Deep learning for image reconstruction

In this lesson we will study the reconstruction of an image from a sequence of a few linear measurements corrupted by noise. This generic problem has many biomedical applications, such as computed tomography, positron emission tomography and optical microscopy. We will first review classical approaches that rely on the optimisation of hand-crafted objective functions. Then, we will introduce modern data-driven approaches that bridge the gap between the former approaches and deep learning. In particular, we will examine unrolled networks that rely on the computation of traditional solutions (e.g. pseudo-inverse, maximum a posteriori). Unrolled networks can be interpreted as iterative schemes optimised with respect to a particular database. We will discuss network variants and learning strategies. Finally, we will focus on an optical problem in which the setup acquires some coefficients of the Hadamard transform of the image of the scene. We will present reconstruction results from experimental datasets acquired under different noise levels.

The lesson will be accompanied by a practical session in which we will address the problem of limited view computed tomography.

Thomas Grenier - CREATIS laboratory, Lyon, France

His 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.

Pierre-Marc Jodoin - University of Sherbrooke, Canada

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 plateform 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/

Jose Dolz - École de Technologie Supérieure, Canada

Prof Jose Dolz is currently Associate Professor at École de technologie supérieure Montreal. His current research focuses on deep learning, medical imaging, optimization and learning strategies with limited supervision. He has authored over 80 fully peer-reviewed papers, many of which are published in the top venues in medical imaging (MICCAI/MedIA/TMI/IPMI/NeuroImage), computer vision (CVPR) and machine learning (ICML, NeurIPS), and organized 5 tutorials in deep learning with limited supervision (MICCAI’19, MICCAI’20, MICCAI’21, MICCAI’22 and ICPR’22). Jose serves regularly as Program Committee for MICCAI and MIDL, and has been recognized as Outstanding reviewer at prestigious conferences (ECCV’20, CVPR’21, CVPR’22, NeurIPS’22).

Carole Frindel - CREATIS laboratory, Lyon, France

Carole Frindel is an Associate Professor at INSA Lyon and at the CREATIS laboratory in Lyon, France. Her research focuses on computational medical imaging, with a particular interest in predicting the outcome of stroke. This task is complex because the lesion visible in imaging evolves up to one month later. For this purpose, I develop new approaches in machine and deep learning, for the fusion, encoding and simulation of multimodal data. I strive to bridge the gap between theory and applications.

Odyssée Merveille - CREATIS laboratory, Lyon France

Odyssée Merveille has been an associate professor at INSA Lyon and at the CREATIS laboratory since 2019.
She received a PhD degree in computer science from the Université Paris-Est in 2016 and was a postdoc at Université de Strasbourg. Her scientific interests include inverse problems and deep learning for medical imaging, in particular for the analysis of vascular networks.

Fabien Millioz - CREATIS laboratory, Lyon, France

Fabien Millioz graduated from the École Normale Supérieure de Cachan, France and received the M.Sc. degree in 2005 and Ph.D. degree in 2009 both in signal processing from the Institut National Polytechnique of Grenoble, France. Since 2011, he is lecturer at University Claude Bernard Lyon 1, and member of the CREATIS lab since 2015.

His research interests are statistical signal processing, fast acquisition, compressed sensing and neural networks.

Bruno Montcel - CREATIS laboratory, Lyon, France

Bruno Montcel is Associate Professor (Maître de Conférences - HDR) at the Université Lyon 1 and the CREATIS lab in Lyon, France. His research focuses on optical imaging methods and experimental set up for the exploration of brain physiology and pathologies. It mainly focuses on intraoperative and point of care hyperspectral optical imaging methods for medical diagnosis and gesture assistance.

Michaël Sdika - CREATIS laboratory, Lyon, France

Michaël Sdika is from 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.