Summary of Med-persam: One-shot Visual Prompt Tuning For Personalized Segment Anything Model in Medical Domain, by Hangyul Yoon et al.
Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain
by Hangyul Yoon, Doohyuk Jang, Jungeun Kim, Eunho Yang
First submitted to arxiv on: 25 Nov 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 Med-PerSAM, a novel one-shot framework designed to overcome limitations in the medical domain when using pre-trained Segment Anything Model (SAM) for image analysis. By leveraging visual prompt engineering and eliminating the need for additional training or human intervention, Med-PerSAM enables the efficient extraction and refinement of visual prompts for enhancing SAM’s performance on 2D medical imaging datasets. The approach addresses common challenges in medical image analysis, such as inaccurate prompt generation and clustering, to achieve improved results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Med-PerSAM is a new way to use pre-trained models like SAM for medical image analysis. Instead of needing lots of training data or human help, Med-PerSAM uses special techniques to create good prompts automatically. This makes it easier for people without medical expertise to work with the model and get better results. |
Keywords
» Artificial intelligence » Clustering » One shot » Prompt » Sam
