  The absence of well-structured large datasets in medical computer vision
results in decreased performance of automated systems and, especially, of deep
learning models. Domain generalization techniques aim to approach unknown
domains from a single data source. In this paper we introduce a novel
framework, named CompStyle, which leverages style transfer and adversarial
training, along with high-level input complexity augmentation to effectively
expand the domain space and address unknown distributions. State-of-the-art
style transfer methods depend on the existence of subdomains within the source
dataset. However, this can lead to an inherent dataset bias in the image
creation. Input-level augmentation can provide a solution to this problem by
widening the domain space in the source dataset and boost performance on
out-of-domain distributions. We provide results from experiments on semantic
segmentation on prostate data and corruption robustness on cardiac data which
demonstrate the effectiveness of our approach. Our method increases performance
in both tasks, without added cost to training time or resources.
