  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 non-medical style source such as artistic paintings, for learning
domain-agnostic visual representations in computational pathology. Style
transfer replaces the low-level texture content of an image with the
uninformative style of randomly selected style source image, while preserving
the original 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
two particular classification tasks in computational pathology.
