Summary of Protosam: One-shot Medical Image Segmentation with Foundational Models, by Lev Ayzenberg et al.
ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
by Lev Ayzenberg, Raja Giryes, Hayit Greenspan
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces ProtoSAM, a novel framework for one-shot medical image segmentation. By combining prototypical networks and SAM, a natural image foundation model, the method initializes a coarse segmentation mask using ALPnet and DINOv2. The prompts are then extracted and input into the Segment Anything Model (SAM), achieving state-of-the-art results on several medical image datasets. This approach enables automated segmentation with only one image example, eliminating the need for fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to quickly segment medical images using just one picture as an example. It combines two types of AI models: prototypical networks and SAM (a natural image foundation model). The method uses one model to create an initial outline of the image, then another model to fine-tune it. This results in highly accurate segmentation without needing to train the models on large datasets. |
Keywords
» Artificial intelligence » Fine tuning » Image segmentation » Mask » One shot » Sam
