Stain Translation

Stain translation in digital pathology aims to computationally convert histological images from one staining protocol to another, mitigating the variability introduced by different staining methods and scanners. Current research focuses on improving the accuracy and structural preservation of translated images using generative adversarial networks (GANs), particularly CycleGANs, often enhanced with techniques like multi-task learning, stain isolation, and self-supervised learning. This work is crucial for improving the generalizability and robustness of deep learning models in digital pathology, enabling more reliable and cost-effective analysis of large histopathology datasets across different laboratories and institutions. Ultimately, stain translation promises to advance diagnostic accuracy and facilitate the development of more widely applicable computational pathology tools.