论文标题
Markov-Chain Monte Carlo使用生成对抗网络对理想观察者的近似
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks
论文作者
论文摘要
在优化信号检测任务的医学成像系统时，已经提倡理想的观察者（IO）性能。但是，IO检验统计量的分析计算通常是棘手的。为了近似IO测试统计量，已经开发了采用Markov-Chain Monte Carlo（MCMC）技术的基于抽样的方法。但是，MCMC技术的当前应用仅限于多种对象模型，例如块状对象模型和二进制纹理模型，并且尚不清楚如何使用其他更复杂的对象模型来实现MCMC方法。采用生成对抗网络（GAN）的深度学习方法具有从图像数据中学习随机对象模型（SOM）的巨大希望。在这项研究中，我们描述了一种通过将MCMC技术应用于通过使用GAN学习的SOM来近似IO的方法。所提出的方法可以使用任意对象模型，可以通过使用gan来学习，从而扩展了MCMC技术的适用性领域。在这项研究中，考虑了信号 - 已知的（SKE）和信号统计（SKS）二进制信号检测任务。将通过提出的方法计算的IO性能与常规MCMC方法计算的方法进行了比较。讨论了提出的方法的优势。
The Ideal Observer (IO) performance has been advocated when optimizing medical imaging systems for signal detection tasks. However, analytical computation of the IO test statistic is generally intractable. To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed. However, current applications of MCMC techniques have been limited to several object models such as a lumpy object model and a binary texture model, and it remains unclear how MCMC methods can be implemented with other more sophisticated object models. Deep learning methods that employ generative adversarial networks (GANs) hold great promise to learn stochastic object models (SOMs) from image data. In this study, we described a method to approximate the IO by applying MCMC techniques to SOMs learned by use of GANs. The proposed method can be employed with arbitrary object models that can be learned by use of GANs, thereby the domain of applicability of MCMC techniques for approximating the IO performance is extended. In this study, both signal-known-exactly (SKE) and signal-known-statistically (SKS) binary signal detection tasks are considered. The IO performance computed by the proposed method is compared to that computed by the conventional MCMC method. The advantages of the proposed method are discussed.
