Summary of Medclip-sam: Bridging Text and Image Towards Universal Medical Image Segmentation, by Taha Koleilat et al.
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
by Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a novel framework, MedCLIP-SAM, for data-efficient medical image segmentation using text prompts. It combines CLIP and SAM models to generate segmentation masks from clinical scans in both zero-shot and weakly supervised settings. The framework employs a new loss function, Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE), to fine-tune the BiomedCLIP model and gScoreCAM for prompt generation. This is achieved by using zero-shot segmentation labels in a weakly supervised paradigm to improve segmentation quality. Three diverse segmentation tasks and medical image modalities are tested, demonstrating excellent accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to look at medical images using text prompts. It uses two existing models, CLIP and SAM, to create a new one called MedCLIP-SAM. This model can take a doctor’s notes or patient information and use it to segment specific parts of the image. The method is tested on three different types of images and shows good results. |
Keywords
» Artificial intelligence » Image segmentation » Loss function » Prompt » Sam » Supervised » Zero shot
