Development of a large-scale grounded vision language dataset for chest CT analysis.

ABSTRACT: Developing generalist foundation model has recently attracted tremendous attention in the field of AI for Medicine, which requires open-source medical image datasets that incorporate diverse supervision signals across various imaging modalities. In this paper, we introduce RadGenome-Chest CT, a comprehensive, large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. Specifically, we leverage the latest powerful universal segmentation model and large language models, to extend the original datasets from the following aspects: organ-level segmentation masks covering 197 categories, which provide intermediate reasoning visual clues for interpretation; 665K multigranularity grounded reports, where each sentence of the report is linked to the corresponding anatomical regio

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12513997 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature