Andréanne Deschênes

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