  Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized
for biopsy visualization-based diagnostic and prognostic assessment of
diseases. Variation in the H&E staining process across different lab sites can
lead to significant variations in biopsy image appearance. These variations
introduce an undesirable bias when the slides are examined by pathologists or
used for training deep learning models. To reduce this bias, slides need to be
translated to a common domain of stain appearance before analysis. We propose a
Self-Attentive Adversarial Stain Normalization (SAASN) approach for the
normalization of multiple stain appearances to a common domain. This
unsupervised generative adversarial approach includes self-attention mechanism
for synthesizing images with finer detail while preserving the structural
consistency of the biopsy features during translation. SAASN demonstrates
consistent and superior performance compared to other popular stain
normalization techniques on H&E stained duodenal biopsy image data.
