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Computational histopathology involves CAD for microscopic analysis of stained histopathological slides to study presence, localization or grading of disease. An important stage in a CAD system, stain color normalization, has been broadly studied. The existing approaches are mainly defined in the context of stain deconvolution and template matching. In this paper, we propose a novel approach to this problem by introducing a parametric, fully unsupervised generative model. Our model is based on end-to-end machine learning in the framework of generative adversarial networks. It can learn a nonlinear transformation of a set of latent variables, which are forced to have a prior Dirichlet distribution and control the color of staining hematoxylin and eosin (H&E) images. By replacing the latent variables of a source image with those extracted from a template image in the trained model, it can generate a new color copy …