论文标题
k-distripution：使用贝叶斯神经网络的参数估计和不确定性定量
Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks
论文作者
论文摘要
定量超声（QUS）允许估计固有的组织特性。 Speckle统计信息是描述超声（US）信封数据的一阶统计数据的QU参数。同源K-Distribution（HK分布）的参数是可以在不同散射条件下模拟包络数据的斑点统计信息。但是，它们需要大量数据才能可靠地估计。因此，找出估计参数的内在不确定性可以帮助我们更好地了解估计参数。在本文中，我们提出了一个贝叶斯神经网络（BNN），以估计HK分布的参数并量化估计量的不确定性。
Quantitative ultrasound (QUS) allows estimating the intrinsic tissue properties. Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data. The parameters of Homodyned K-distribution (HK-distribution) are the speckle statistics that can model the envelope data in diverse scattering conditions. However, they require a large amount of data to be estimated reliably. Consequently, finding out the intrinsic uncertainty of the estimated parameters can help us to have a better understanding of the estimated parameters. In this paper, we propose a Bayesian Neural Network (BNN) to estimate the parameters of HK-distribution and quantify the uncertainty of the estimator.
