{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5(1)"],"submitter":["Fan J"],"pubmed_abstract":["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 "],"journal":["Communications engineering"],"pagination":["9"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12800261"],"repository":["biostudies-literature"],"pubmed_title":["A foundation model for rock thin-section images analysis."],"pmcid":["PMC12800261"],"pubmed_authors":["Pan X","Liu Y","Fan J","Bao J","Lv T","Zhang R","Li L","Yu X","Di Y"],"additional_accession":[]},"is_claimable":false,"name":"A foundation model for rock thin-section images analysis.","description":"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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-11T05:19:55.126Z","creation":"2026-06-11T03:07:47.221Z"},"accession":"S-EPMC12800261","cross_references":{"pubmed":["41353266"],"doi":["10.1038/s44172-025-00565-5"]}}