  In whole slide imaging, commonly used staining techniques based on
hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate
different 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 medical
centers. Virtually generating IHC images from H&E using deep neural networks
thus becomes an attractive alternative. Deep generative models such as
CycleGANs learn a semantically-consistent mapping between two image domains,
while emulating the textural properties of each domain. They are therefore a
suitable choice for stain transfer applications. However, they remain fully
unsupervised, and possess no mechanism for enforcing biological consistency in
stain transfer. In this paper, we propose an extension to CycleGANs in the form
of a region of interest discriminator. This allows the CycleGAN to learn from
unpaired datasets where, in addition, there is a partial annotation of objects
for which one wishes to enforce consistency. We present a use case on whole
slide images, where an IHC stain provides an experimentally generated signal
for metastatic cells. We demonstrate the superiority of our approach over prior
art in stain transfer on histopathology tiles over two datasets. Our code and
model are available at https://github.com/jcboyd/miccai2022-roigan.
