  We propose a virtual staining methodology based on Generative Adversarial
Networks to map histopathology images of breast cancer tissue from H&E stain to
PHH3 and vice versa. We use the resulting synthetic images to build
Convolutional Neural Networks (CNN) for automatic detection of mitotic figures,
a strong prognostic biomarker used in routine breast cancer diagnosis and
grading. We propose several scenarios, in which CNN trained with synthetically
generated histopathology images perform on par with or even better than the
same baseline model trained with real images. We discuss the potential of this
application to scale the number of training samples without the need for manual
annotations.
