用因果推理提升病理图像肿瘤检测在分布外数据上的泛化能力
CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts
- 基于前门准则设计转换策略，利用语义特征并控制混杂因素
- 在CAMELYON17和私有数据集上均实现最高7%的性能提升
- 适合关注医学图像泛化与因果建模的研究者
组织病理学中的域偏移常由采集流程或数据来源差异引起，严重制约深度学习模型的泛化能力。现有方法多依赖对齐特征分布或引入统计变异来建模相关性，却忽视了因果关系。本文提出一种新型因果推理框架，利用语义特征并缓解混杂因子影响。通过设计符合前门准则的转换策略，显式纳入中介变量和观测切片信息。在CAMELYON17数据集和一个私有病理数据集上验证，该方法在未见域上表现稳定，相比现有基线，在两个数据集上均实现最高7%的性能提升。结果表明，因果推理是应对病理图像分析中域偏移问题的有力工具。
原文摘要 · Abstract (English)
Domain shift in histopathology, often caused by differences in acquisition processes or data sources, poses a major challenge to the generalization ability of deep learning models. Existing methods primarily rely on modeling statistical correlations by aligning feature distributions or introducing statistical variation, yet they often overlook causal relationships. In this work, we propose a novel causal-inference-based framework that leverages semantic features while mitigating the impact of confounders. Our method implements the front-door principle by designing transformation strategies that explicitly incorporate mediators and observed tissue slides. We validate our method on the CAMELYON17 dataset and a private histopathology dataset, demonstrating consistent performance gains across unseen domains. As a result, our approach achieved up to a 7% improvement in both the CAMELYON17 dataset and the private histopathology dataset, outperforming existing baselines. These results highlight the potential of causal inference as a powerful tool for addressing domain shift in histopathology image analysis.
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