风格迁移作为数据增强：评估乳腺X线摄影中的非配对图像到图像转换模型
Style transfer as data augmentation: evaluating unpaired image-to-image translation models in mammography
AI总结：
本文研究乳腺X线摄影中风格迁移作为数据增强的方法，比较CycleGAN与SynDiff在三个数据集上的非配对图像转换性能，并系统分析常用评估指标的优缺点，强调需多指标综合评估模型表现。
摘要：
多项研究表明，深度学习模型能够学习从乳腺X线照片（乳房X射线图像）中检测乳腺癌。然而，过拟合和泛化能力差的问题阻碍了其在临床中的常规应用。在某一患者群体数据上训练的模型，由于扫描技术或患者特征的差异所导致的数据域差异，可能在另一群体上表现不佳。数据增强技术可通过改变现有样本，扩展训练数据中特征表示的多样性，从而用于提高泛化能力。图像到图像转换模型是一种能够将来自一个数据集图像的特征表示（即风格）施加到另一个数据集上的方法。然而，评估模型性能并非易事，尤其是在缺乏真实标签的情况下（这是医学成像中的常见现实）。在此，我们描述了评估风格迁移算法时应考虑的一些关键方面，强调了常用指标的优缺点，以及在实际实施时需要注意的重要因素。我们考虑了两种生成模型：循环一致性生成对抗网络（CycleGAN）和基于扩散的SynDiff模型。我们学习了三个乳腺X线摄影数据集之间的非配对图像到图像转换。我们强调，模型性能中不理想的方面可能决定某些指标的适用性，并提供了一些分析，表明各种指标在多大程度上评估了模型性能的不同方面。我们强调需要使用多个指标来全面评估模型性能。
英文摘要：
Several studies indicate that deep learning models can learn to detect breast cancer from mammograms (X-ray images of the breasts). However, challenges with overfitting and poor generalisability prevent their routine use in the clinic. Models trained on data from one patient population may not perform well on another due to differences in their data domains, emerging due to variations in scanning technology or patient characteristics. Data augmentation techniques can be used to improve generalisability by expanding the diversity of feature representations in the training data by altering existing examples. Image-to-image translation models are one approach capable of imposing the characteristic feature representations (i.e. style) of images from one dataset onto another. However, evaluating model performance is non-trivial, particularly in the absence of ground truths (a common reality in medical imaging). Here, we describe some key aspects that should be considered when evaluating style transfer algorithms, highlighting the advantages and disadvantages of popular metrics, and important factors to be mindful of when implementing them in practice. We consider two types of generative models: a cycle-consistent generative adversarial network (CycleGAN) and a diffusion-based SynDiff model. We learn unpaired image-to-image translation across three mammography datasets. We highlight that undesirable aspects of model performance may determine the suitability of some metrics, and also provide some analysis indicating the extent to which various metrics assess unique aspects of model performance. We emphasise the need to use several metrics for a comprehensive assessment of model performance.
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