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相关实验视频

Updated: Jun 14, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

启用Shapley值的渐进型伪袋增大用于全幻灯片图像分类.

Renao Yan, Qiehe Sun, Cheng Jin

IEEE transactions on medical imaging

|September 2, 2024

概括

这项研究引入了一种新的计算病理学方法,使用Shapley值进行更准确的全幻灯片图像分类. 该方法改善了多实例学习中的实例重要性得分,增强了诊断洞察力.

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:

由于千兆像素分辨率和有限的注释,全幻灯片图像 (WSI) 的分类具有挑战性.
多实例学习 (MIL) 是WSI的弱监督方法,但从袋级标签中提炼实例级信息是困难的.
传统的MIL方法常常会出现注意力分布偏差和实例识别不准确的问题.

研究的目的:

开发一种改进的方法,例如MIL中的重要性评分 (IIS),用于WSI分类.
提高MIL模型在计算病理学中的准确性和可解释性.
解决传统基于注意力的IIS估计的局限性.

主要方法:

提出了一种新的方法,灵感来自于合作游戏理论,利用Shapley值来评估实例贡献,以改进IIS估计.
使用注意力机制加速Shapley值计算,同时保持增强的实例识别和优先级.
引入了一个基于估计的IIS进行渐进的伪袋分配的框架,以促进平衡的注意力分配.

主要成果:

在CAMELYON-16,BRACS,TCGA-LUNG和TCGA-BRCA数据集上表现出高于最先进的方法的性能.
在WSI分类中实现了增强的可解释性和类明智的洞察力.

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验证了Shapley值和伪袋分配在计算病理学中的MIL的有效性.

结论:

提出的沙普利基于价值的MIL方法显著提高了WSI分类的准确性和可解释性.
该方法有效地完善实例级信息,克服传统注意力机制的局限性.
这项工作为推进数字病理学中弱监督学习提供了一个有希望的方向.