CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios

Understand

3D medical vision-language (VL) pretraining has shown potential in radiology by leveraging large-scale multimodal datasets with CT-report pairs.

• However, existing methods primarily rely on a global VL alignment directly adapted from 2D scenarios.
• The entire 3D image is transformed into one global embedding, resulting in a loss of sparse but critical semantics essential for accurately aligning with the corresponding diagnosis.
• To address this limitation, we propose CT-GLIP, a 3D Grounded Language-Image Pretrained model that constructs fine-grained CT-report pairs to enhance \textit{grounded} cross-modal contrastive learning, effectively aligning grounded visual features with precise textual descriptions.