揭示病理图像分类中因果推断方法的双通道解耦机制
Demonstration of the common dual-channel feature decoupling characteristic of front-door mediation causal inference methods in whole-slice image classification
- 提出双通道独立特征提取思路，分离诊断与非诊断特征
- 通道间特征差异越大，越能消除错误关联，提升分类准确率
- 为数字病理图像分析提供新理论视角，适合医学图像研究者
基于前门干预和多实例学习（MIL）的因果推断方法推动了数字病理学中全切片图像（WSI）的分析。这些方法通过调整细微证据子图像的特征分布，正确建立其与整体诊断之间的关联。本文提出并验证两个假设：1）因果推断MIL引入独立分类通道，可有效完成WSI分类；2）新通道与基线通道提取特征的差异越大，越能有效消除虚假关联。该假设揭示了因果推断MIL的核心机制：通过叠加并行、独立的通道，增加深层特征多样性，从而消除整体诊断与非诊断子图像间的错误关联。基于此，我们在乳腺癌和非小细胞肺癌数据集上评估了多种因果推断MIL方法。该假设为因果推断在WSI分析中的应用提供了新的理论视角。
原文摘要 · Abstract (English)
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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