扫描不变的mamba与差异化序列对比学习在计算病理学.
IEEE transactions on medical imaging
|February 27, 2026
概括
扫描不变的Mamba与差异化序列对比学习 (SMDC-MIL) 解决了Mamba.
科学领域:
- 计算病理学计算病理学
- 人工智能在组织病理学中的应用.
- 用于医学成像的深度学习
背景情况:
- 整体幻灯片图像 (WSIs) 在组织病理学中,由于高分辨率和粗略标签,对多个实例学习 (MIL) 提出了挑战.
- 应用于WSI分类的Mamba架构将MIL视为长序列任务,但对扫描模式敏感,与MIL对扫描不变预测的需求相冲突.
研究的目的:
- 开发一种新的基于Mamba的MIL方法用于组织病理学分析,实现扫描不变的预测.
- 为了使袋级特征学习能够独立于基于Mamba的MIL的输入扫描模式.
主要方法:
- 拟议的扫描不变的Mamba与差异化序列对比学习 (SMDC-MIL).
- 引入了使用实例重新排列,增强和掩盖来模拟扫描变化的差异化序列生成机制.
- 实现了差异化序列对比学习架构,以在各种序列中强制执行一致的袋级表示.
主要成果:
- 在4个计算病理学任务和10个数据集中,SMDC-MIL展示了最先进的性能.
- 该方法有效地减轻了扫描模式对Mamba预测的影响.
- 实现了扫描不变特征学习,以实现强大的WSI分类.
结论:
- 在组织病理学中,SMDC-MIL成功地适应了Mamba用于扫描不变的MIL.
- 拟议的方法提高了基于Mamba的WSI分类的可靠性和性能.
- 这项工作为应用Mamba在计算病理学中提供了一个有希望的方向.
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