Tackling stain variability using CycleGAN-based stain augmentation

Our findings suggest that stain variability can be tackled more effectively by augmenting data by it than by following the commonly used approach of normalizing the stain. However, the applicability of this approach providing only a rather slight performance increase has to be weighted against an additional carbon footprint.