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.
View on arXiv@article{lu2025_2504.10080,
title={ Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction },
author={ Yucheng Lu and Shunxin Wang and Dovile Juodelyte and Veronika Cheplygina },
journal={arXiv preprint arXiv:2504.10080},
year={ 2025 }
}