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.

本文介绍了一种名为 CompStyle 的新型框架，利用样式转换和对抗训练以及高级输入复杂性增强来扩展域空间和处理未知的分布，以提高自动化系统和深度学习模型的性能。实验证明我们的方法在肾脏数据的语义分割和心脏数据的损坏鲁棒性上都能提高性能，而无需额外的培训时间或资源。