Talks and presentations

Diffusion models in medical imaging

April 23, 2025

Invited speaker, Spring school on Deep Learning in Medical Imaging (DLMI), Lyon, France

Diffusion-based generative models have recently emerged as a powerful framework for modeling complex data distributions, especially in high-dimensional domains like medical imaging. In this lecture, I introduced the core concepts behind Denoising Diffusion Probabilistic Models (DDPM), a prominent class of diffusion models grounded in stochastic processes. I then illustrated their practical relevance through applications to data augmentation, semantic segmentation, and anomaly detection, highlighting how such models can generate anatomically plausible and diverse samples.

Is the problem of medical image segmentation a thing of the past ?

July 07, 2024

Invited speaker, International Conference on the use of Computers in Radiation therapy (ICCR), Lyon, France

For several years now, deep learning (DL) techniques have been successfully applied to the segmentation of medical imaging. Several pilot studies initially showed the superiority of DL over conventional methods using databases of around one hundred patients. However, these initial results have raised other issues. Firstly, the generalization of DL methods to larger databases with high variability in shapes, vendors, image quality, and pathologies poses a challenge. Secondly, there is difficulty in producing reliable expert annotations on large databases. These challenges cast doubt on the ability of DL methods to provide a definitive solution to the segmentation problem in medical imaging. However, recent advancements in artificial intelligence (AI) research have revolutionized computer vision and image processing. This has led to the development of new powerful and more generic tools such as foundation models and reinforcement learning methods. Will the application of these tools in our field lead to the definitive resolution of segmentation in medical imaging in the near future? This is the burning question that I will address in my presentation.

Most applied and promising AI methods in cardiac imaging

June 13, 2024

Invited speaker, French JFICV congress: Journées francophones imagerie cardio-vasculaire, Pessac, France

AI tools have demonstrated their efficiency in numerous cardiac applications for several years now. In this presentation, I describe (in french) four examples of applications where AI has revolutionized the status quo and enabled significant advancements towards precise, robust, and reproducible automation of routine clinical measurements, i.e. standardized acquisitions, extraction of anatomical indices, extraction of functional indices and uncertainty modeling

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.

Olivier Bernard