论文标题
评估在医学成像中建立基于生成对抗网络的随机图像模型的程序
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
论文作者
论文摘要
现代生成模型，例如生成对抗网络（GAN），对多个医学成像领域（例如无条件的医学图像综合，图像恢复，重建和翻译以及成像系统的优化）具有巨大的希望。但是，使用gans建立随机图像模型（SIM）的程序仍然是通用的，并且不能解决与医学成像相关的特定问题。在这项工作中，使用模拟逼真的血管造影图像中的逼真的容器来评估使用gans建立模拟的程序的规范模拟模拟模拟的模拟模拟。将基于GAN的SIM卡与规范SIM进行比较，其基于其重现那些对所考虑的特定医学现实SIM的有意义的统计数据的能力。结果表明，使用经典指标和医学相关指标评估gan可能会导致有关训练有素gan的忠诚度的不同结论。这项工作强调了开发客观指标以评估gan的需求。
Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several areas of medical imaging, such as unconditional medical image synthesis, image restoration, reconstruction and translation, and optimization of imaging systems. However, procedures for establishing stochastic image models (SIMs) using GANs remain generic and do not address specific issues relevant to medical imaging. In this work, canonical SIMs that simulate realistic vessels in angiography images are employed to evaluate procedures for establishing SIMs using GANs. The GAN-based SIM is compared to the canonical SIM based on its ability to reproduce those statistics that are meaningful to the particular medically realistic SIM considered. It is shown that evaluating GANs using classical metrics and medically relevant metrics may lead to different conclusions about the fidelity of the trained GANs. This work highlights the need for the development of objective metrics for evaluating GANs.
