Summary of Semantic Segmentation Refiner For Ultrasound Applications with Zero-shot Foundation Models, by Hedda Cohen Indelman et al.
Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models
by Hedda Cohen Indelman, Elay Dahan, Angeles M. Perez-Agosto, Carmit Shiran, Doron Shaked, Nati Daniel
First submitted to arxiv on: 25 Apr 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 The proposed prompt-less segmentation method harnesses the ability of segmentation foundation models to segment abstract shapes, addressing the performance degradation of segmentation models in low-data regimes. The approach uses coarse semantic segmentation masks as input and a zero-shot prompt-able foundation model as an optimization target. This method is demonstrated on a segmentation findings task (pathologic anomalies) in ultrasound images, showcasing a larger performance gain as the training set size decreases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to segment medical images without needing many labeled examples. The approach uses special models that can learn to identify shapes even if they’re not shown exact pictures of what to look for. This helps when there aren’t many labeled images available, which is often the case in medical imaging analysis. The method is tested on ultrasound images and shows better performance as the amount of training data decreases. |
Keywords
» Artificial intelligence » Optimization » Prompt » Semantic segmentation » Zero shot
