AI 中文总结
研究在全切片图像分类中，提出并证明关于因果推理MIL的两个假设，即引入独立分类通道及增加通道特征差异可消除错误关联，通过在相关数据集上评估几种因果推理MIL，为因果推理应用于WSI分析提供新视角。
AI 中文摘要
使用前门干预和多实例学习（MIL）的因果推理推动了数字病理学中全切片图像（WSI）的分析。这些方法调整细微证据子图像的特征分布，以将它们与WSI级诊断正确关联。我们提出并证明了2个用于评估此类方法的假设：1）因果推理MIL引入了一个有效完成WSI分类的独立分类通道；2）新通道和基线通道提取的特征之间的更大差异会提高消除错误关联的有效性。该假设描述了因果推理MIL的核心：通过增加深度特征多样性来叠加并行、独立的通道，以消除WSI级诊断和非诊断证据子图像之间的错误关联。基于这些假设，我们在乳腺癌和非小细胞肺癌数据集上评估了几种因果推理MIL。该假设为将因果推理应用于WSI分析提供了新的理论视角。
英文摘要
Causal inference using front door intervention and multi-instance learning (MIL) has advanced the analysis of Whole Slide Images (WSI) in digital pathology. These methods adjust feature distributions of subtle evidence sub-images to correctly associate them with WSI-level diagnoses. We propose and prove 2 hypotheses for evaluating such methods: 1) Causal inference MIL introduces an independent classification channel that effectively completes WSI classification; 2) Greater difference between features extracted by the new and baseline channels increases effectiveness in eliminating false correlations. This hypothesis describes the core of causal inference MILs: overlaying parallel, independent channels to eliminate false associations between WSI-level diagnostic and non-diagnostic evidence sub-images by increasing deep feature diversity. Based on these hypotheses, we evaluated several causal inference MILs on breast cancer and non-small cell lung cancer datasets. This hypothesis provides a new theoretical perspective for applying causal inference to WSI analysis.
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