Summary of Simsam: Zero-shot Medical Image Segmentation Via Simulated Interaction, by Benjamin Towle et al.
SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction
by Benjamin Towle, Xin Chen, Ke Zhou
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 Segment Anything Model (SAM) has shown impressive zero-shot segmentation capabilities through a semi-automatic annotation setup. Researchers are exploring its application to medical imaging, where expert annotations are costly, privacy restrictions are strict, and model generalization is often poor. However, there is inherent uncertainty in medical images due to unclear object boundaries, low-contrast media, and differences in expert labeling style. SAM struggles in zero-shot settings to annotate medical image contours accurately, requiring significant manual correction. To address this, we introduce Simulated Interaction for Segment Anything Model (SimSAM), which generates candidate masks using simulated user interaction and aggregates them to output the most compatible mask. SimSAM can be used directly on top of SAM without additional training. Our method achieves up to a 15.5% improvement in contour segmentation accuracy compared to zero-shot SAM across three public medical imaging datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Segment Anything Model (SAM) is a new way for computers to look at pictures and find the important parts. People are excited about using it with medical images because it could help doctors and researchers without needing lots of extra work or special permission. But, there’s a problem – the pictures can be unclear, fuzzy, or labeled differently by different people. SAM has trouble finding the right edges in these pictures, so we came up with a new idea called Simulated Interaction for Segment Anything Model (SimSAM). It helps SAM find the best answer by pretending to look at the picture and trying out different answers. This makes SAM better at finding the important parts of medical images. |
Keywords
» Artificial intelligence » Generalization » Mask » Sam » Zero shot
