面向改进泛化性的风格增强特征域混合组织病理图像分析
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
AI总结：
本文提出一种基于自适应实例归一化的特征域风格混合方法，以较低计算成本生成风格增强图像，用于提升组织病理图像分析模型的域泛化能力，实验表明其性能相当或优于现有风格迁移增强方法。
摘要：
组织病理图像对于医学诊断和治疗规划至关重要，但由于组织制备、染色和成像流程的差异，使用机器学习对其进行准确解读可能具有挑战性。域泛化旨在通过使学习模型能够泛化到新的数据集或人群来应对此类限制。基于风格迁移的数据增强是一种新兴技术，可用于提高机器学习模型在组织病理图像上的泛化能力。然而，现有的基于风格迁移的方法可能计算开销较大，并且它们依赖艺术风格，这可能对模型准确性产生负面影响。在本研究中，我们提出了一种特征域风格混合技术，该技术使用自适应实例归一化来生成图像的风格增强版本。我们将所提出的方法与现有的基于风格迁移的数据增强方法进行了比较，发现尽管所需的计算量和时间更少，但其性能相当或更优。我们的结果证明了特征域统计混合在组织病理图像分析学习模型泛化中的潜力。
英文摘要：
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.
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