This repo contains the checkpoints for OminiAbnorm-CT, a multi-modal generative model for grounded abnormality analysis on CT images from multiple planes and all human body regions. It supports three representative tasks:
- Visual prompted report generation: Interpret an abnormality marked by a red bounding box, ellipse, contour, or cropped region.
- Grounded report generation: Ground and interpret all abnormalities on the CT image.
- Text-guided grounded report generation: Detect, ground and interpret a specific abnormality on the CT image.
It is built on OminiAbnorm-CT-14K, the first large-scale dataset designed for abnormality grounding and description on multi-plane whole-body CT imaging. It contains 14.5K CT images with grounding annotation for 19K abnormal findings. Each abnormal finding is further linked to the detailed description in the report, and categorized according to a comprehensive hierarchical taxonomy.
Check our paper and github repo for usage and more details.
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