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Suboptimal generalization of machine learning models on unseen data is a keychallenge which hampers the clinical applicability of such models to medicalimaging. Although various methods such as domain adaptation and domaingeneralization have evolved to combat this challenge, learning robust andgeneralizable representations is core to medical image understanding, andcontinues to be a problem. Here, we propose STRAP (Style TRansfer Augmentationfor histoPathology), a form of data augmentation based on random style transferfrom non-medical style source such as artistic paintings, for learningdomain-agnostic visual representations in computational pathology. Styletransfer replaces the low-level texture content of an image with theuninformative style of randomly selected style source image, while preservingthe original high-level semantic content. This improves robustness to domainshift and can be used as a simple yet powerful tool for learningdomain-agnostic representations. We demonstrate that STRAP leads tostate-of-the-art performance, particularly in the presence of domain shifts, ontwo particular classification tasks in computational pathology.

Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation