Summary of Ct-glip: 3d Grounded Language-image Pretraining with Ct Scans and Radiology Reports For Full-body Scenarios, by Jingyang Lin et al.
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios
by Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, Ling Zhang
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel medical vision-language pretraining method called CT-GLIP is introduced to effectively capture essential semantics from 3D imaging in full-body scenarios. This approach constructs organ-level image-text pairs to enhance multimodal contrastive learning, aligning grounded visual features with precise diagnostic text. The method is trained on a multimodal CT dataset and demonstrates superior performance over the standard CLIP framework across zero-shot and fine-tuning scenarios, using both CNN and ViT architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Med-VLP connects medical images to relevant textual descriptions. This paper extends Med-VLP from 2D chest X-rays to 3D full-body scenarios with CT scans. The new method, called CT-GLIP, pairs CT images with text reports for organs. It trains on a big dataset of CT images and texts. This helps the model learn to identify organs and abnormalities using natural language without being trained on specific abnormality examples. The results show that CT-GLIP is better than another popular approach at identifying organs and abnormalities. |
Keywords
» Artificial intelligence » Cnn » Fine tuning » Pretraining » Semantics » Vit » Zero shot
