A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy
Foundation Models (FMs) have dramatically increased the potential and power of deep learning algorithms through general capacities over a variety 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.