[2604.05960] A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis

Computer Science > Machine Learning arXiv:2604.05960 (cs) [Submitted on 7 Apr 2026] Title:A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis Authors:Sk Miraj Ahmed, Yuewei Lin, Chuntian Cao, Shinjae Yoo, Xinpei Wu, Won-Il Lee, Nikhil Tiwale, Dan N. Le, Thi Thu Huong Chu, Jiyoung Kim, Kevin G. Yager, Chang-Yong Nam View a PDF of the paper titled A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis, by Sk Miraj Ahmed and 11 other authors View PDF HTML (experimental) Abstract:Scanning Electron Microscopy (SEM) is indispensable in modern materials science, enabling high-resolution imaging across a wide range of structural, chemical, and functional investigations. However, SEM imaging remains constrained by task-specific models and labor-intensive acquisition processes that limit its scalability across diverse applications. Here, we introduce the first foundation model for SEM images, pretrained on a large corpus of multi-instrument, multi-condition scientific micrographs, enabling generalization across diverse material systems and imaging conditions. Leveraging a self-supervised transformer architecture, our model learns rich and transferable representations that can be fine-tuned or adapted to a wide range of downstream tasks. As a compelling demonstration, we focus on defocus-to-focus image translation-an essential yet underexplored challenge in automated microscopy pipelines. Our method not only restores fo...