简单、安全且被忽视：通过统计色彩匹配重拾可持续的领域泛化
Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching
- University of Bamberg（班贝格大学）
- xAILab
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出高效可解释的Colorist策略，通过RGB空间统计色彩匹配提升医学图像分类器的领域泛化能力，在多类医学数据集上显著优于现有方法，且安全低耗。
AI中文摘要：
开发与部署阶段间的硬件变化、色彩差异及患者特征变化常会导致训练好的医学图像分类器失效。现有解决方案存在不足：标准色彩抖动提供的多样性不足，而深度生成式风格迁移算法会生成幻觉特征、破坏临床相关结构且浪费大量计算资源。为解决此问题，我们重新审视经典统计色彩匹配并将其改造为Colorist，这是一种高效的数据增强策略，直接在RGB色彩空间应用全局均值-标准差匹配。我们证明这种无训练、完全可解释的方法可安全生成结构完整的领域变化，在结构保真度和色彩对齐上优于深度生成模型。在分布外的组织病理学、外周血、皮肤病学和视网膜数据集上，它比最先进的领域泛化正则化器将平衡准确率提升最多9%，比未增强的基线提升最多13%。此外，由于在增强循环中不使用神经网络，Colorist保留了解剖结构、最小化了碳足迹且可无缝集成到标准数据加载器中。这些发现共同确立了统计匹配作为安全、可解释但被忽视的替代方案，可用于提升临床场景的鲁棒性，替代深度架构。源代码可在此URL获取。
英文摘要：
Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classifiers. Existing remedies fall short: standard color jittering provides insufficient diversity, while deep generative style transfer algorithms hallucinate features, destroy clinically relevant structures, and waste massive compute resources. To address this, we revisit classical statistical color matching and repurpose it as Colorist, a highly efficient data augmentation strategy that applies global mean-standard deviation matching directly in the RGB color space. We demonstrate that this training-free, fully interpretable approach safely generates structurally intact domain variations, outperforming deep generative models in structural fidelity and color alignment. Across out-of-distribution histopathology, peripheral blood, dermatology, and retinal datasets, it improves balanced accuracy by up to +9% over state-of-the-art domain generalization regularizers and by +13% over an unaugmented baseline. Moreover, by avoiding neural networks in the augmentation loop, Colorist preserves anatomical structure, minimizes carbon footprint, and integrates seamlessly into standard dataloaders. Together, these findings establish statistical matching as a safe, interpretable, yet overlooked alternative to deep architectures for clinical robustness. Source code is available at https://github.com/sdoerrich97/colorist.
