Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which can negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to generate style-augmented versions of images. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring less computation and time. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.

本研究提出了一种基于特征域风格混合的技术，利用自适应实例归一化生成具有风格增强版本的图像。通过与现有的基于风格迁移的数据增强方法进行比较，发现该方法在计算和时间上要求较低的情况下表现相似或更好。研究结果展示了特征域统计混合在组织病理学图像分析学习模型的泛化中的潜力。

利用样式增强特征域混合的组织病理学图像分析以提高泛化能力