FluoGen: An Open-Source Generative Foundation Model for Fluorescence Microscopy Image Enhancement and Analysis

Fluorescence microscopy, empowered by artificial intelligence (AI), has shown great promise in advancing life science. However, the high cost and complexity of sample preparation limit the availability of training data, constraining the performance of AI-based models. Here, we present FluoGen, a diffusion-based generative foundation model designed to improve AI-based fluorescence image processing under data-constrained conditions. By pretraining on 3.5 million fluorescence images with a reformulated learning objective, FluoGen learns rich biological representations and mitigates inherent biases in conventional diffusion models. We demonstrate that FluoGen can serve as a backbone for image enhancement, enabling models to recover cellular and subcellular structures with substantially limited samples. Furthermore, FluoGen can reduce the training data required by existing AI-based analysis models to approximately 2% of the original amount while boosting the performance of state-of-the-art methods without architectural modifications. We anticipate that FluoGen serves as a foundation tool for advancing AI applications in fluorescence imaging.