论文标题
关于损害其潜在表示形式的生成图像，我们能学到什么？
What can we learn about a generated image corrupting its latent representation?
论文作者
论文摘要
生成对抗网络（GAN）为图像到图像翻译问题提供了有效的解决方案，从而允许医学成像中的新可能性。他们可以以低成本将图像从一种成像方式转换为另一种成像方式。对于未配对的数据集，它们主要依赖于周期损失。尽管它在学习基础数据分布方面有效，但它可能导致输入数据和输出数据之间的差异。这项工作的目的是调查以下假设：我们可以根据甘纳克（Gans）瓶颈中的潜在表示来预测图像质量。我们通过用噪声破坏潜在表示并产生多个输出来实现这一目标。它们之间的差异程度被解释为表示的强度：潜在表示越强大，腐败导致输出图像的变化越少。我们的结果表明，我们提出的方法具有i）预测合成图像的不确定部分，ii）确定可能对下游任务（例如肝分割任务）可能不可靠的样本。
Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effectiveness in learning the underlying data distribution, it can lead to a discrepancy between input and output data. The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck. We achieve this by corrupting the latent representation with noise and generating multiple outputs. The degree of differences between them is interpreted as the strength of the representation: the more robust the latent representation, the fewer changes in the output image the corruption causes. Our results demonstrate that our proposed method has the ability to i) predict uncertain parts of synthesized images, and ii) identify samples that may not be reliable for downstream tasks, e.g., liver segmentation task.
