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Frédéric Beaupré

Doctorat - Université Laval
Superviseur⋅e principal⋅e
Christian Gagné
Sujets de recherche
Apprentissage multimodal
Apprentissage profond
Neurosciences computationnelles
Vision par ordinateur
GitHub

Publications

A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy
Anthony Bilodeau
Frédéric Beaupré
Julia Chabbert
Kamylle Thériault
Andréanne Deschênes
Jean-Michel Bellavance
Koraly Lessard
Renaud Bernatchez
Paul De Koninck
Christian Gagné
Flavie Lavoie-Cardinal
Foundation Models (FMs) have dramatically increased the potential and power of deep learning algorithms through general capacities over a va… (voir plus)riety of tasks. The performance increase they offer is obtained without elaborated specific trainings for domains such as natural language processing and computer vision. However, their application in specialized fields like biomedical imaging and fluorescence microscopy remains difficult due to distribution shifts and the scarcity of high-quality annotated datasets. The high cost of data acquisition and the requirement for in-domain expertise further exacerbate this challenge in microscopy. To address this we introduce STED-FM, a foundation model specifically designed for super-resolution STimulated Emission Depletion (STED) microscopy. STED-FM leverages a Vision Transformer architecture trained at scale with Masked Autoencoding on a new dataset of nearly one million STED images. STED-FM learns expressive latent representations without requiring extensive annotations, yielding robust performance across diverse downstream microscopy image analysis tasks. Unsupervised experiments demonstrate the discriminative structure of its learned latent space. These representations can be leveraged for multiple downstream applications, including fully supervised classification and segmentation with reduced annotation requirements. Moreover, STED-FM representations enhance the performance of deep learning–based image denoising and improve the quality of images generated by diffusion models, enabling latent attribute manipulation for the data-driven discovery of subtle nanostructures and phenotypes, as well as algorithmic super-resolution. Moreover, its powerful structure retrieval capabilities are integrated into automated STED microscopy acquisition pipelines, paving the way for smart microscopy. In sum, we demonstrate that STED-FM lays a robust foundation for state-of-the-art algorithms across a wide array of tasks, establishing it as a highly valuable and scalable resource for researchers in super-resolution microscopy.
2025-06-05
bioRxiv (prépublication)
doi.org
Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning
Frédéric Beaupré
Anthony Bilodeau
Theresa Wiesner
Gabriel Leclerc
Mado Lemieux
Gabriel Nadeau
Katrine Castonguay
Bolin Fan
Simon Labrecque
Renée Hložek
Paul De Koninck
Christian Gagné
Flavie Lavoie-Cardinal
Ca 2+ imaging methods are widely used for studying cellular activity in the brain, allowing detailed ana… (voir plus)lysis of dynamic processes across various scales. Enhanced by high-contrast optical microscopy and fluorescent Ca 2+ sensors, this technique can be used to reveal localized Ca 2+ fluctuations within neurons, including in sub-cellular compartments, such as the dendritic shaft or spines. Despite advances in Ca 2+ sensors, the analysis of miniature Synaptic Calcium Transients (mSCTs), characterized by variability in morphology and low signal-to-noise ratios, remains challenging. Traditional threshold-based methods struggle with the detection and segmentation of these small, dynamic events. Deep learning (DL) approaches offer promising solutions but are limited by the need for large annotated datasets. Positive Unlabeled (PU) learning addresses this limitation by leveraging unlabeled instances to increase dataset size and enhance performance. This approach is particularly useful in the case of mSCTs that are scarce and small, associated with a very small proportion of the foreground pixels. PU learning significantly increases the effective size of the training dataset, improving model performance. Here, we present a PU learning-based strategy for detecting and segmenting mSCTs. We evaluate the performance of two 3D deep learning models, StarDist-3D and 3D U-Net, which are well established for the segmentation of small volumetric structures in microscopy datasets. By integrating PU learning, we enhance the 3D U-Net’s performance, demonstrating significant gains over traditional methods. This work pioneers the application of PU learning in Ca 2+ imaging analysis, offering a robust framework for mSCT detection and segmentation. We also demonstrate how this quantitative analysis pipeline can be used for subsequent mSCTs feature analysis. We characterize morphological and kinetic changes of mSCTs associated with the application of chemical long-term potentiation (cLTP) stimulation in cultured rat hippocampal neurons. Our data-driven approach shows that a cLTP-inducing stimulus leads to the emergence of new active dendritic regions and differently affects mSCTs subtypes.
2024-07-07
bioRxiv (prépublication)
doi.org