用因果引导的噪声扰动提升医学影像模型泛化能力
Causally Guided Gaussian Perturbations for Out-Of-Distribution Generalization in Medical Imaging
- 基于视觉变换器生成软因果掩码，指导图像不同区域加噪强度
- 在Camelyon17数据集上超越现有OOD方法，提升模型鲁棒性
- 轻量级设计适合临床部署，结果可解释性强
在真实世界中部署深度学习模型时，分布外（OOD）泛化仍是核心挑战，尤其在生物医学影像领域，分布偏移既细微又普遍。现有方法多依赖复杂的生成模型或对抗训练来追求域不变性，但常忽略泛化背后的因果机制。本文提出一种轻量级框架——因果引导高斯扰动（CGP），通过视觉变压器生成的软因果掩码，对输入图像施加空间变化的噪声：背景区域施加更强扰动，前景区域施加较弱扰动，从而引导模型依赖因果相关特征而非虚假关联。在具有挑战性的WILDS基准数据集Camelyon17上的实验表明，该方法持续优于当前最先进的OOD基线，凸显了因果扰动在实现可靠、可解释泛化方面的潜力。
原文摘要 · Abstract (English)
Out-of-distribution (OOD) generalization remains a central challenge in deploying deep learning models to real-world scenarios, particularly in domains such as biomedical images, where distribution shifts are both subtle and pervasive. While existing methods often pursue domain invariance through complex generative models or adversarial training, these approaches may overlook the underlying causal mechanisms of generalization.In this work, we propose Causally-Guided Gaussian Perturbations (CGP)-a lightweight framework that enhances OOD generalization by injecting spatially varying noise into input images, guided by soft causal masks derived from Vision Transformers. By applying stronger perturbations to background regions and weaker ones to foreground areas, CGP encourages the model to rely on causally relevant features rather than spurious correlations.Experimental results on the challenging WILDS benchmark Camelyon17 demonstrate consistent performance gains over state-of-the-art OOD baselines, highlighting the potential of causal perturbation as a tool for reliable and interpretable generalization.
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