Olivier Bernard

Olivier Bernard

Professor at the university of Lyon (INSA), France and deputy director of the CREATIS laboratory

Talks and presentations

On the integration of robust AI-based image information for continuous patient stratification

February 03, 2024

Invited speaker, Korean Society of Echocardiography congress, Seoul, South Korea

AI paradigm now enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provide fine-grained information that physicians consider, in conjunction with more global variables from the clinical record, to assess patients’ condition. In this presentation, I introduced a recently developed transformer-based framework that takes into account all descriptors extracted from medical records and echocardiograms. The framework aims to learn the representation of a challenging cardiovascular pathology, specifically hypertension. I showed that for descriptors whose interactions with hypertension are well documented, patterns are consistent with prior physiological knowledge, paving the way for further clinical studies to better understand this disease.

Physical simulations for deep learning: applications to image formation and motion estimation

September 08, 2023

Satellite symposium talk, IEEE Ultrasonic symposium (IUS) conference, Montreal, Canada

In recent years, artificial intelligence (AI) techniques have shown remarkable success in the field of medical imaging. While supervised learning approaches currently stand as the most effective methods, they rely heavily on ground truth data usually obtained from manual annotations, which often poses challenges across diverse applications. In this presentation, I introduced a comprehensive pipeline aimed at generating realistic simulations of echocardiographic image sequences to be used as inputs for supervised learning algorithms. I showcased their application in two different areas: tissue motion estimation in traditional imaging and ultra-fast cardiac image reconstruction.

Segmentation in echocardiography: is the problem finally solved ?

September 06, 2023

Seminar, LIVIA laboratory, Montreal, Canada

Deep learning methods have allowed major advances in many specific areas such as echocardiogram analysis, view classification, recognition and segmentation of anatomical structures, user guidance or even automatic report generation. Moreover, the combination of these different techniques allows the deployment of complete and fully automated processing chains, making it possible to increase the reliability of clinical measurements and facilitate the use of ultrasound scanners outside of hospitals. In this talk, I described the various advances we have made using the deep learning paradigm for echocardigraphy image segmentation.

Generative models - variational auto-encoder

April 18, 2023

Invited talk, Deep learning for medical imaging school, Lyon, France

In this presentation, I taught the formalism of the variational auto-encoder and emphasized the importance of creating specialized latent spaces using various examples. I then described the auto-encoder formalism and highlighted the additional advantages of the variational approach. I concluded by showing how we have exploited this formalism in order to i) provide guarantees on the segmented anatomical structures; ii) provide guarantees on the temporal consistency of the segmented shapes; iii) model the uncertainty of the segmentation algorithm.