Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images

Due to differences in tissue preparations, staining protocols and scanner models, stain colors of digitized histological images are excessively diverse. Color normalization is almost a necessary procedure for quantitative digital pathology analysis. Though several color normalization methods have been proposed, most of them depend on selection of representative templates and may fail in regions not matching the templates. We propose an enhanced cycle-GAN based method with a novel auxiliary input for the generator by computing a stain color matrix for every H&E image in the training set. The matrix guides the translation in the generator, and thus stabilizes the cycle consistency loss. We applied our proposed method as a pre-processing step for a breast metastasis classification task on a dataset from five medical centers and achieved the highest performance compared to other color normalization methods. Furthermore, our method is template-free and may be applied to other datasets without finetuning.