Christian Gagné

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 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.

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