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。

医学图像中的域泛化复杂风格转换

Complex Style Image Transformations for Domain Generalization in Medical  Images

Poor generalization performance caused by distribution shifts in unseen
domains often hinders the trustworthy deployment of deep neural networks. Many
domain generalization techniques address this problem by adding a domain
invariant regularization loss terms during training. However, there is a lack
of modular software that allows users to combine the advantages of different
methods with minimal effort for reproducibility. DomainLab is a modular Python
package for training user specified neural networks with composable
regularization loss terms. Its decoupled design allows the separation of neural
networks from regularization loss construction. Hierarchical combinations of
neural networks, different domain generalization methods, and associated
hyperparameters, can all be specified together with other experimental setup in
a single configuration file. Hierarchical combinations of neural networks,
different domain generalization methods, and associated hyperparameters, can
all be specified together with other experimental setup in a single
configuration file. In addition, DomainLab offers powerful benchmarking
functionality to evaluate the generalization performance of neural networks in
out-of-distribution data. The package supports running the specified benchmark
on an HPC cluster or on a standalone machine. The package is well tested with
over 95 percent coverage and well documented. From the user perspective, it is
closed to modification but open to extension. The package is under the MIT
license, and its source code, tutorial and documentation can be found at
this https URL

深度神经网络的信任部署经常受到未知领域分布偏移引起的泛化性能差的影响。DomainLab 是一个模块化的 Python 包，用于训练用户指定的神经网络，并具有可组合的正则化损失项。