论文标题
利用光声断层扫描的贝叶斯反面问题中的变异自动编码器
Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
论文作者
论文摘要
在使用机器学习方法和成像中利用机器学习方法的兴趣越来越大。但是，大多数工作都集中在图像重建问题上，有关反问题的完整解决方案的研究数量有限。在这项工作中，我们研究了基于机器学习的方法，用于光声断层扫描的贝叶斯逆问题。我们开发了一种使用基于变异自动编码器的方法来估算光声断层扫描中后验分布的方法。通过数值模拟评估该方法，并使用贝叶斯方法将其与反问题的解决方案进行了比较。
There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach.
