可信的图像到图像转换：评估非配对训练场景中的不确定性校准
Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios
AI总结：
本文针对医学图像非配对训练场景，评估了cycleGAN和SynDiff两种图像到图像转换模型，提出利用不确定性量化评估模型可信度及校准质量的方案，促进无真实标签领域模型的可信应用。
摘要：
乳腺X光筛查是检测乳腺癌的有效方法，有助于早期诊断。然而，目前需要手动检查图像给医疗系统带来了沉重负担，激发了对自动化诊断方案的需求。基于深度神经网络的技术在一些研究中已被证明有效，但其过拟合倾向导致泛化能力差和误诊的巨大风险，阻碍了其在临床环境中的广泛应用。已有基于非配对神经风格迁移模型的数据增强方案被提出，这些方案通过在缺乏配对训练数据（同一组织的图像以两种图像风格呈现）的情况下，使训练图像特征的表示多样化，从而提高泛化能力。但这些模型同样容易出现各种病理问题，且在没有真实标签/大型数据集的情况下（这在医学成像中很常见）评估其性能具有挑战性。在此，我们考虑了两种框架/架构：基于GAN的cycleGAN，以及最近开发的基于扩散的SynDiff。我们评估了它们在从三个开放获取乳腺X光摄影数据集和一个非医学图像数据集解析出的图像块上训练时的性能。我们考虑使用不确定性量化来评估模型的可信度，并提出了一种在非配对训练场景中评估校准质量的方案。这最终有助于在通常无法获得真实标签的领域中促进图像到图像转换模型的可信使用。
英文摘要：
Mammographic screening is an effective method for detecting breast cancer, facilitating early diagnosis. However, the current need to manually inspect images places a heavy burden on healthcare systems, spurring a desire for automated diagnostic protocols. Techniques based on deep neural networks have been shown effective in some studies, but their tendency to overfit leaves considerable risk for poor generalisation and misdiagnosis, preventing their widespread adoption in clinical settings. Data augmentation schemes based on unpaired neural style transfer models have been proposed that improve generalisability by diversifying the representations of training image features in the absence of paired training data (images of the same tissue in either image style). But these models are similarly prone to various pathologies, and evaluating their performance is challenging without ground truths/large datasets (as is often the case in medical imaging). Here, we consider two frameworks/architectures: a GAN-based cycleGAN, and the more recently developed diffusion-based SynDiff. We evaluate their performance when trained on image patches parsed from three open access mammography datasets and one non-medical image dataset. We consider the use of uncertainty quantification to assess model trustworthiness, and propose a scheme to evaluate calibration quality in unpaired training scenarios. This ultimately helps facilitate the trustworthy use of image-to-image translation models in domains where ground truths are not typically available.
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