论文标题
评估生成对抗网络学习规范医学图像统计的能力
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
论文作者
论文摘要
近年来，生成的对抗网络（GAN）在医学成像中的潜在应用中获得了极大的流行，例如医学图像合成，恢复，重建，翻译以及客观的图像质量评估。尽管在产生高分辨率，感知逼真的图像方面取得了令人印象深刻的进展，但尚不清楚现代甘斯是否可靠地学习对下游医学成像应用有意义的统计数据。在这项工作中，研究了与客观评估图像质量相关的最先进的GAN学习规范随机图像模型（SIMS）的统计数据的能力。结果表明，尽管受雇的GAN成功地学习了正在考虑的特定医疗模拟人生的几个基本的一阶和二阶统计数据，并且具有高感知质量的图像，但它未能正确地了解与这些SIMS相关的几个每图像统计数据，突显了迫切需要在客观的图像质量测量中评估医疗图像的迫切需要。
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.
