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Histopathology relies on the analysis of microscopic tissue images todiagnose disease. A crucial part of tissue preparation is staining whereby adye is used to make the salient tissue components more distinguishable.However, differences in laboratory protocols and scanning devices result insignificant confounding appearance variation in the corresponding images. Thisvariation increases both human error and the inter-rater variability, as wellas hinders the performance of automatic or semi-automatic methods. In thepresent paper we introduce an unsupervised adversarial network to translate(and hence normalize) whole slide images across multiple data acquisitiondomains. Our key contributions are: (i) an adversarial architecture whichlearns across multiple domains with a single generator-discriminator networkusing an information flow branch which optimizes for perceptual loss, and (ii)the inclusion of an additional feature extraction network during training whichguides the transformation network to keep all the structural features in thetissue image intact. We: (i) demonstrate the effectiveness of the proposedmethod firstly on H\&E slides of 120 cases of kidney cancer, as well as (ii)show the benefits of the approach on more general problems, such as flexibleillumination based natural image enhancement and light source adaptation.

MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss