Flavie Lavoie‐Cardinal

PhenoMe provides an open-source framework for analyzing microscopy images using foundation model-derived embeddings and experimental metadata to enable phenotyping and data exploration.

Astro-BEATS detects and segments miniature synaptic calcium transients in fluorescence imaging using astronomical transient detection techniques, outperforming threshold-based methods and enabling efficient training of deep learning models.

This paper introduces a method to improve STED microscopy by using fluorescence lifetime unmixing and filtering to enhance resolution and accuracy in two-species imaging.

This paper introduces STED-FM, a foundation model for STED microscopy that learns robust representations without extensive annotations, improving downstream tasks like classification, segmentation, and image denoising.

This paper introduces a PU learning-based method for detecting and segmenting miniature synaptic calcium transients, showing improved performance over traditional methods in cultured rat neurons.

This paper develops a simulation platform, pySTED, to aid in creating AI-assisted super-resolution microscopy techniques by generating realistic data for training and testing AI models.

This paper introduces TA-GAN, a deep learning model that enhances image resolution in optical nanoscopy by incorporating auxiliary tasks, improving accuracy and enabling online imaging assistance.

MICRA-Net is a weakly-supervised neural network that uses a simple classification task to perform segmentation, detection, and enumeration on microscopy images with limited annotated data.

The paper uses deep learning to analyze F-actin structures in hippocampal neurons, showing activity-dependent remodeling in dendrites but not axons.

The paper investigates how tubulin conformation and mitochondrial positioning affect anterograde transport along microtubules in vitro and in vivo.