StainGAN: Learning a structural preserving translation for white blood cell images

Abstract

AbstractAnalysis of white blood cells in blood smear images plays a vital role in computer‐aided diagnosis for the analysis and treatment of many diseases. However, different techniques for blood smear preparation result in images with large appearance variations, which limits the performance of large‐scale machine learning algorithms. In this paper, we propose StainGAN, an image translation framework to transform the conventional Wright‐stained white blood cell images into their rapidly‐stained counterpart. Moreover, we designed a cluster‐based learning strategy that does not require manual annotations and a multi‐scale discriminator that incorporates a richer hierarchy of the spatial context to generate sharper images with better semantic consistency. Experimental results on multiple real‐world datasets prove the effectiveness of our proposed framework. Moreover, we show that the transformed images from StainGAN can be used to boost the downstream segmentation performance under the label‐limiting scenario.