Summary of Global-local Medical Sam Adaptor Based on Full Adaption, by Meng Wang (school Of Electronic and Information Engineering Liaoning Technical University Xingcheng City et al.
Global-Local Medical SAM Adaptor Based on Full Adaption
by Meng Wang, Yarong Feng, Yongwei Tang, Tian Zhang, Yuxin Liang, Chao Lv
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 global medical SAM adaptor (GMed-SA) and its combination with a partial adaption manner model, Med-SA, aim to improve universal semantic segmentation, particularly in medical image segmentation. The GMed-SA adapts the segment anything model (SAM) globally, while the global-local medical SAM adaptor (GLMed-SA) combines both full and partial adaptation. Experimental results on the 2D melanoma segmentation dataset show that GLMed-SA outperforms state-of-the-art methods on various evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes new models to improve medical image segmentation by adapting a universal semantic segmentation model, SAM. The authors develop two new adaptors: GMed-SA and Med-SA. They test these models on a public dataset and find that the combination of both models works best. This makes it easier to segment medical images accurately. |
Keywords
» Artificial intelligence » Image segmentation » Sam » Semantic segmentation
