Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models -- denoising diffusion probabilistic models (DDPMs) -- demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs). To date, these claims have been evaluated using either ensemble-based methods designed for natural images, or conventional measures of image quality such as structural similarity. However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work. To address this, a systematic assessment of the ability of DDPMs to learn spatial context relevant to medical imaging applications is reported for the first time. A key aspect of the studies is the use of stochastic context models (SCMs) to produce training data. In this way, the ability of the DDPMs to reliably reproduce spatial context can be quantitatively assessed by use of post-hoc image analyses. Error-rates in DDPM-generated ensembles are reported, and compared to those corresponding to a modern GAN. The studies reveal new and important insights regarding the capacity of DDPMs to learn spatial context. Notably, the results demonstrate that DDPMs hold significant capacity for generating contextually correct images that are `interpolated' between training samples, which may benefit data-augmentation tasks in ways that GANs cannot.

翻译：扩散模型已成为一类流行的深度生成模型（DGM）。文献中声称，与生成对抗网络（GAN）相比，一类扩散模型——去噪扩散概率模型（DDPM）——展现出更优的图像合成性能。迄今为止，这些主张的评估要么采用为自然图像设计的集成方法，要么使用结构相似性等传统图像质量度量。然而，仍迫切需要理解DDPM在多大程度上能可靠学习医学成像领域相关信息——本文中称之为“空间上下文”。为此，首次系统评估了DDPM学习医学成像应用相关空间上下文的能力。研究的关键在于使用随机上下文模型（SCM）生成训练数据。通过这种方式，可借助事后图像分析定量评估DDPM可靠再现空间上下文的能力。报告了DDPM生成集成中的错误率，并与现代GAN对应的错误率进行了比较。研究揭示了关于DDPM学习空间上下文能力的新重要见解。值得注意的是，结果表明DDPM在生成“内插于”训练样本之间的上下文正确图像方面具有显著能力，这可能在数据增强任务中发挥GAN无法实现的优势。