一个多尺度线性时间编码器,用于全幻灯片图像分析
ArXiv
|February 12, 2026
概括
我们开发了MARBLE,这是一个基于Mamba的全新框架,用于整张幻灯片的图像分析. 这种高效的方法可以提高多尺度生物医学图像任务的准确性和概括性.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 生物医学图像分析
背景情况:
- 全幻灯片图像 (WSI) 分析是复杂的,因为千兆像素大小和多个放大.
- 现有的多个实例学习 (MIL) 方法经常使用单级分析.
- 变压器模型面临着具有二次注意力成本的计算挑战.
研究的目的:
- 介绍MARBLE,一个纯粹基于Mamba的MIL框架,用于高效的WSI分析.
- 允许并行处理多个放大级别,以实现集成推理.
- 为基于注意力的架构提供可扩展和模块化的替代方案.
主要方法:
- 开发了多尺度自适应循环生物医学线性时间编码器 (MARBLE).
- 采用基于Mamba的状态空间模型进行线性时间序列建模.
- 实现了多个WSI尺度的并行处理,使用粗到细的推理.
主要成果:
- 在五个公共数据集中,MARBLE 显示了显著的性能改善.
- 在曲线下的面积 (AUC) 中实现了高达6.9%的增加.
- 在准确度方面提高了20.3%,C指数提高了2.3%.
结论:
- 马尔伯为高效和可通用的多尺度WSI分析建立了新的标准.
- 基于Mamba的方法提供了一个可扩展的解决方案,克服了现有方法的局限性.
- 马尔伯有效地捕捉了与最小参数开销的交叉尺度依赖关系.
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