Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
This addresses domain shift in medical imaging for improved model generalization, but is incremental as it builds on existing harmonization techniques.
The paper tackles the problem of domain shift in medical image analysis caused by different X-ray machine vendors by showing that conventional linear normalization methods fail to address nonlinear image dynamics. They propose Global Deep Curve Estimation (GDCE), which reformulates harmonization as exposure correction using a polynomial function and domain discriminator, improving model transparency compared to black-box methods.
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.