EM Generalist: A physics-driven diffusion foundation model for electron microscopy

Electron microscopy (EM) is an indispensable tool for visualizing biological structures at the nanoscale. However, high-fidelity EM imaging is often time-consuming, and 3D isotropic volume EM (vEM) remains impractical for large-scale analysis. Recent supervised deep learning approaches have partially mitigated these limitations, but they rely heavily on large paired datasets for training and struggle to generalize to out-of-distribution samples or new tasks. Here, we introduce EM Generalist, a diffusion-based generative foundation model trained on over 1.7 million high-quality EM samples from Internet-scale public datasets, and is coupled with a Bayesian inversion framework that incorporates physics-driven image degradation models during sampling to enable generalization across diverse data distributions and tasks. We demonstrated that EM Generalist is capable of addressing various tasks, including denoising, super-resolution, defocus correction, and 3D isotropic volume reconstruction without requiring paired training data for these tasks. This capability enables high-quality, zero-shot reconstructions across various imaging scenarios and diverse sample types.