Summary of Robust Box Prompt Based Sam For Medical Image Segmentation, by Yuhao Huang et al.
Robust Box Prompt based SAM for Medical Image Segmentation
by Yuhao Huang, Xin Yang, Han Zhou, Yan Cao, Haoran Dou, Fajin Dong, Dong Ni
First submitted to arxiv on: 31 Jul 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) is a machine learning model that can achieve good segmentation performance when given high-quality box prompts. However, its performance drops significantly if the box prompts are low quality. To address this issue, researchers propose a novel Robust Box prompt based SAM (RoBox-SAM) that ensures SAM’s segmentation performance regardless of the box prompt quality. RoBox-SAM has three key components: a prompt refinement module to improve low-quality prompts, a prompt enhancement module to generate point prompts for better segmentation, and a self-information extractor to encode prior information from the input image. The researchers validate the efficacy of RoBox-SAM on a large medical segmentation dataset, which includes 99,299 images, 5 modalities, and 25 organs/targets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a machine learning model called SAM that can cut out specific parts from pictures. But there’s a problem – the model only works well if it gets high-quality instructions (called box prompts). If the instructions are poor quality, the model doesn’t work as well. To solve this issue, scientists created a new version of SAM called RoBox-SAM that uses three special tools: one to improve low-quality instructions, another to create better instructions from scratch, and a third to use prior knowledge about the picture being segmented. The researchers tested RoBox-SAM on a huge dataset of medical images and found it worked really well. |
Keywords
» Artificial intelligence » Machine learning » Prompt » Sam
