  Despite notable advancements, the integration of deep learning (DL)
techniques into impactful clinical applications, particularly in the realm of
digital histopathology, has been hindered by challenges associated with
achieving robust generalization across diverse imaging domains and
characteristics. Traditional mitigation strategies in this field such as data
augmentation and stain color normalization have proven insufficient in
addressing this limitation, necessitating the exploration of alternative
methodologies. To this end, we propose a novel generative method for domain
generalization in histopathology images. Our method employs a generative,
self-supervised Vision Transformer to dynamically extract characteristics of
image patches and seamlessly infuse them into the original images, thereby
creating novel, synthetic images with diverse attributes. By enriching the
dataset with such synthesized images, we aim to enhance its holistic nature,
facilitating improved generalization of DL models to unseen domains. Extensive
experiments conducted on two distinct histopathology datasets demonstrate the
effectiveness of our proposed approach, outperforming the state of the art
substantially, on the Camelyon17-wilds challenge dataset (+2%) and on a second
epithelium-stroma dataset (+26%). Furthermore, we emphasize our method's
ability to readily scale with increasingly available unlabeled data samples and
more complex, higher parametric architectures. Source code is available at
https://github.com/sdoerrich97/vits-are-generative-models .
