Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching

2026-08-19

AI Summary

本文针对医疗图像分类器在部署中因硬件和颜色变化导致的性能下降问题，提出了一种基于统计色彩匹配的数据增强方法Colorist，以提高模型的泛化能力和结构保真度。

Abstract

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.

Problem

• medical image classifiers
• color variations
• deep generative style transfer

Innovation

• statistical color matching
• data augmentation
• domain generalization
• structural fidelity
• computational efficiency