通过四条因果路径干预，提升医疗影像模型的公平性与鲁棒性。
CIPHER: Causal Intervention Pathways for Healthcare Equity and Robustness
- 构建医疗影像生成的因果模型，识别四条敏感属性影响路径。
- 在胸部X光与皮肤镜数据上，最差群体误差降低35.8%。
- 适合关注医疗AI公平性、可解释性研究者使用。
用于医学诊断的深度学习模型在不同敏感子群体（如种族、性别）间常表现出显著性能差异，即使平均准确率较高也是如此。尽管生成式数据增强提供了一种缓解途径，但现有方法通常仅处理敏感属性与图像特征之间的单一或双通道依赖关系。本文通过结构因果模型形式化医疗影像形成过程，揭示敏感属性实际上通过四条不同的因果路径影响图像内容，这一结构性复杂性此前被忽视。基于此洞察，我们提出CIPHER（因果干预路径框架），系统性地干预全部四条因果路径。为此，CIPHER采用具备无分类器引导与空文本反演的扩散模型架构，实现患者特异性解剖结构的忠实重建，同时支持精准可编辑的反事实生成，以打破敏感属性依赖链。我们在胸部X光和皮肤镜基准测试中评估了CIPHER，涵盖标准与分布偏移的数据场景。通过多路径干预策略，相比疾病条件合成基线，模型在最差群体上的性能差距平均降低了35.8%，同时提升了整体诊断准确率。
原文摘要 · Abstract (English)
Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.g., race, sex), even when average accuracy is high. While generative data augmentation offers a route to mitigate this, existing strategies are suboptimal; they typically address only one or two dependency channels between sensitive attributes and image features. We formalize the medical image formation process via a structural causal model, revealing that sensitive attributes actually influence image content through four distinct pathways-a structural complexity neglected by prior works. Based on this insight, we introduce CIPHER (Causal Intervention Pathways for Healthcare Equity and Robustness), a framework designed to systematically intervene on all four causal paths. To achieve this, CIPHER utilizes a diffusion backbone equipped with classifier-free guidance and null-text inversion. This technical design enables the faithful reconstruction of patient-specific anatomy while allowing for the precise, editable synthesis of counterfactuals required to break sensitive dependency chains. We tested CIPHER using chest X-ray and dermoscopy benchmarks across both standard and shifted data distributions. By employing a multi-pathway intervention strategy, our model reduced worst-group disparities by an average of 35.8% compared to disease-conditioned synthesis baselines, while also improving total diagnostic accuracy
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