Abstract
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 the help of a ``domain discriminator'', aiming to improve model transparency in downstream tasks compared to existing black-box methods.
Abstract (translated)
在这篇论文中，我们探讨了传统图像增强技术如何能提高医学影像分析中的模型鲁棒性。通过将常用的归一化方法应用于不同供应商提供的图像，并研究这些方法对迁移学习中模型泛化能力的影响，我们发现简单的线性变换无法解决特定领域内图像动态变化的非线性特征问题。为了解决这一问题，我们将图像校准任务重新定义为曝光调整问题，并提出了一种名为全局深度曲线估计（Global Deep Curve Estimation, GDCE）的方法来减少特定领域的曝光差异。GDCE通过预定义的多项式函数进行增强，并在“领域鉴别器”的帮助下进行训练，旨在与现有的黑盒方法相比，在下游任务中提升模型的透明度。
URL
https://arxiv.org/abs/2504.10080
