  Suboptimal generalization of machine learning models on unseen data is a key
challenge which hampers the clinical applicability of such models to medical
imaging. Although various methods such as domain adaptation and domain
generalization have evolved to combat this challenge, learning robust and
generalizable representations is core to medical image understanding, and
continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation
for histoPathology), a form of data augmentation based on random style transfer
from artistic paintings, for learning domain-agnostic visual representations in
computational pathology. Style transfer replaces the low-level texture content
of images with the uninformative style of randomly selected artistic paintings,
while preserving high-level semantic content. This improves robustness to
domain shift and can be used as a simple yet powerful tool for learning
domain-agnostic representations. We demonstrate that STRAP leads to
state-of-the-art performance, particularly in the presence of domain shifts, on
a particular classification task of predicting microsatellite status in
colorectal cancer using digitized histopathology images.
