Training Set Design for Uneven Illumination Correction in High-Resolution Whole Slide Images

Uneven illumination correction is considered a critical pre-processing step in creating digital images from optical microscopes, particularly in whole-slide imaging (WSI). While deep learning-based methods have suggested new possibilities, they often struggle with generalizing to unseen images and require substantial computational resources. The most common approach for training deep neural networks in this field relies on patch-based processing, which may overlook the global illumination distribution, leading to inconsistencies in correction. This study aimed to identify a key limitation in deep learning models for uneven illumination correction, highlighting the importance of preserving the original image resolution and incorporating a global view of illumination patterns to enhance generalization. To address this, we proposed a new training set design strategy that optimizes neural network performance while utilizing computational resources effectively. Our approach ensures a more uniform correction across entire WSI slides, reducing artifacts and improving image consistency. The proposed strategy enhances model robustness and scalability, making deep learning-based illumination correction more practical for clinical and research applications.

Keywords:

Uneven Illumination Correction , Artifacts , Pathology , Deep Learning , Training Set