biostudies-literatureUnknown5(1)Fan JRock thin-section image analysis is a fundamental task in geological and mineralogical research. Traditional methods rely on visual inspection by experts using optical microscopes, which are inherently subjective, experience-dependent, and time-consuming. Here, we introduce RoImAI, a vision foundation model specifically designed for rock thin-section microscopy images, enabling rapid and precise rock segmentation, identification, and lithology report generation. A large-scale dataset of rock thin-section microscopy images comprising 30,336 images and approximately two million rock particles from 17 different regions was constructed to develop and validate RoImAI. RoImAI leverages Transformer-based deep learning techniques to achieve high-precision segmentation across multi-center datasets Communications engineering9https://www.ebi.ac.uk/biostudies/studies/S-EPMC12800261biostudies-literatureA foundation model for rock thin-section images analysis.PMC12800261Pan XLiu YFan JBao JLv TZhang RLi LYu XDi YfalseA foundation model for rock thin-section images analysis.Rock thin-section image analysis is a fundamental task in geological and mineralogical research. Traditional methods rely on visual inspection by experts using optical microscopes, which are inherently subjective, experience-dependent, and time-consuming. Here, we introduce RoImAI, a vision foundation model specifically designed for rock thin-section microscopy images, enabling rapid and precise rock segmentation, identification, and lithology report generation. A large-scale dataset of rock thin-section microscopy images comprising 30,336 images and approximately two million rock particles from 17 different regions was constructed to develop and validate RoImAI. RoImAI leverages Transformer-based deep learning techniques to achieve high-precision segmentation across multi-center datasets 2025-01-01T00:00:00Z2025 Dec2026-06-11T05:19:55.126Z2026-06-11T03:07:47.221ZS-EPMC128002614135326610.1038/s44172-025-00565-5