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硬负样采矿用于整个幻灯片图像分类

Wentao Huang1, Xiaoling Hu2, Shahira Abousamra1

  • 1Stony Brook University, Stony Brook, NY, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|June 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新方法,用于在微调过程中通过挖掘硬负面样本来进行弱监督的整片图像分类,从而改善特征表示和降低成本. 一个新的补丁智能排名损失提高了多个实例的学习性能.

关键词:
硬样本采矿 硬样本采矿自我训练 自我训练整个幻灯片图像的图像

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 机器学习在医学中的应用

背景情况:

  • 由于缺少补丁级标签和显著的计算需求,低监督的全幻灯片图像 (WSI) 分类存在挑战.
  • 当前的方法通常依赖于在多个实例学习 (MIL) 框架内自主监督的补丁智能的特征表示.
  • 使用伪标签的现有微调方法主要集中在高质量的正补丁选择上.

研究的目的:

  • 为了增强特征表示和降低计算成本在弱监督的WSI分类.
  • 在微调过程中引入挖掘硬负样的策略.
  • 开发一种新的补丁智能排名损失函数,以提高MIL性能.

主要方法:

  • 在特征表示的微调阶段实施硬负样采矿.
  • 开发和应用了一种针对MIL的新型补丁智能排序损失函数,适用于MIL.
  • 在两个公开可用的整个幻灯片图像数据集上验证拟议的方法.

主要成果:

  • 通过硬负样本挖掘来证明特征表示的改进.
  • 对WSI分类任务的整体培训成本大幅降低.
  • 验证拟议的补丁智能排名损失提高MIL性能,如实验结果所示.

结论:

  • 采矿硬负样本的拟议方法有效地改善了特征表示,并降低了弱监督的WSI分类中的计算成本.
  • 新的补丁智能排名损失函数为利用MIL框架内的硬负样本提供了一种优越的方法.
  • 这些发现表明了更高效和有效的计算病理学分析的有希望的方向.