解决不同厂家X光片图像差异问题，提升医学影像模型泛化能力
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
- 将图像统一化重构为曝光校正问题，用多项式函数非线性调整
- 在多个设备数据集上使模型准确率提升3.2%，跨厂商迁移性能显著改善
- 方法可解释性强，适合需要透明性的医疗AI应用
本文研究传统图像增强对医学影像分析中模型鲁棒性的影响。通过在不同厂商的图像上应用常见归一化方法，并考察其对迁移学习中模型泛化能力的影响，我们发现域间图像动态的非线性特征无法通过简单线性变换解决。为此，我们将图像统一化任务重新定义为曝光校正问题，提出全局深度曲线估计（GDCE）方法，以减少域间曝光不匹配。GDCE通过预定义的多项式函数实现增强，并引入域判别器进行训练，相比现有黑箱方法，提升了下游任务中的模型可解释性。
原文摘要 · Abstract (English)
In this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from various vendors and studying their influence on model generalization in transfer learning, we show that the nonlinear characteristics of domain-specific image dynamics cannot be addressed by simple linear transforms. To tackle this issue, we reformulate the image harmonization task as an exposure correction problem and propose a method termed Global Deep Curve Estimation (GDCE) to reduce domain-specific exposure mismatch. GDCE performs enhancement via a pre-defined polynomial function and is trained with a "domain discriminator", aiming to improve model transparency in downstream tasks compared to existing black-box methods.
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