全在SAM:从弱注释到像素智能的核心细分与基于提示符的微调
Can Cui1, Ruining Deng1, Quan Liu1
1Computer Science, Vanderbilt University, Nashville, USA.
概括
全在SAM管道通过使用Segment Anything Model (SAM) 进行自动注释和模型微调来简化生物医学图像细分,消除了手动提示,以获得高效,高性能的结果.
科学领域:
- 计算机视觉 计算机视觉
- 医学图像分析 医学图像分析
- 人工智能的人工智能
背景情况:
- 细分任何模型 (SAM) 提供了强大的零射击细分能力.
- 在SAM中手动提示对生物医学应用来说是资源密集的.
- 有效的细分对于推进医学图像分析至关重要.
研究的目的:
- 引入一个全在SAM的自动化管道,用于使用SAM进行生物医学图像细分.
- 为了消除在SAM推断过程中需要手动提示的需要.
- 提高医疗成像中的AI开发工作流程的效率和性能.
主要方法:
- 利用SAM从弱提示 (点,边界框) 来自动生成像素级注释.
- 员工生成注释来微调SAM模型.
- 在使用MONUSEG数据集的核心细分任务上评估了管道.
主要成果:
- 全在SAM管道的性能超过了核心细分的最先进方法.
- 微调SAM的弱点/少数注释实现了性能与强大的像素注释具有竞争力.
- 证明了SAM的自动注释和微调的有效性.
结论:
- 全在SAM管道显著提高生物医学图像细分的效率.
- 使用SAM进行自动注释和微调,为手动细分提供了可行的替代方案.
- 这种方法有望加速AI在医学成像领域的发展.
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