RadGenome Chest CT: A Grounded Vision Language Dataset for Chest CT Analysis Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing these models is their reliance on dataset scaling, which emphasizes the requirements on developing open source medical image datasets that incorporate diverse supervision signals across various imaging modalities. 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 and large language models, to extend the original datasets (over 25,692 non contrast 3D chest CT volume and reports from 20,000 patients) from the following aspects: (i) organ level segmentation masks covering 197 categories, which provide intermediate reasoning visual clues for interpretation; (ii) 665 K multi granularity grounded reports, where each sentence of the report is linked to the corresponding anatomical region of CT volume in the form of a segmentation mask; (iii) 1.3 M grounded VQA pairs, where questions and ans…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy