  Deep learning models that are trained on histopathological images obtained
from a single lab and/or scanner give poor inference performance on images
obtained from another scanner/lab with a different staining protocol. In recent
years, there has been a good amount of research done for image stain
normalization to address this issue. In this work, we present a novel approach
for the stain normalization problem using fast neural style transfer coupled
with adversarial loss. We also propose a novel stain transfer generator network
based on High-Resolution Network (HRNet) which requires less training time and
gives good generalization with few paired training images of reference stain
and test stain. This approach has been tested on Whole Slide Images (WSIs)
obtained from 8 different labs, where images from one lab were treated as a
reference stain. A deep learning model was trained on this stain and the rest
of the images were transferred to it using the corresponding stain transfer
generator network. Experimentation suggests that this approach is able to
successfully perform stain normalization with good visual quality and provides
better inference performance compared to not applying stain normalization.
