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In whole slide imaging, commonly used staining techniques based onhematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuatedifferent aspects of the tissue landscape. In the case of detecting metastases,IHC provides a distinct readout that is readily interpretable by pathologists.IHC, however, is a more expensive approach and not available at all medicalcenters. Virtually generating IHC images from H&E using deep neural networksthus becomes an attractive alternative. Deep generative models such asCycleGANs learn a semantically-consistent mapping between two image domains,while emulating the textural properties of each domain. They are therefore asuitable choice for stain transfer applications. However, they remain fullyunsupervised, and possess no mechanism for enforcing biological consistency instain transfer. In this paper, we propose an extension to CycleGANs in the formof a region of interest discriminator. This allows the CycleGAN to learn fromunpaired datasets where, in addition, there is a partial annotation of objectsfor which one wishes to enforce consistency. We present a use case on wholeslide images, where an IHC stain provides an experimentally generated signalfor metastatic cells. We demonstrate the superiority of our approach over priorart in stain transfer on histopathology tiles over two datasets. Our code andmodel are available at https://github.com/jcboyd/miccai2022-roigan.

Region-guided CycleGANs for Stain Transfer in Whole Slide Images