定量超声中同源性 K 分布的不确定性分解与误差边际检测

Dorsa Ameri, Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
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引用次数: 0

摘要

最近,有人使用贝叶斯神经网络(BNN)来处理定量超声(QUS)中的同调 K 分布(HK 分布)参数估计问题。事实证明,贝叶斯神经网络能在不影响准确性和精确度的前提下,显著减少基于斑点统计的 QUS 的计算时间。此外,BNN 还能估计特征的不确定性,从而指导临床医生对报告特征值的信任度。贝叶斯建模中的总预测不确定性可分解为认识不确定性(模型参数的不确定性)和估计不确定性(数据固有的不确定性)两部分。通过分解预测不确定性,我们可以了解导致总不确定性的因素。在本研究中,我们提出了一种方法,用于计算 BNN 在模拟和实验数据中估计的香港分布参数($\alpha$ 和 $k$)的认识不确定性和时间不确定性。此外,我们研究了预测误差与这两种不确定性之间的关系,揭示了这些不确定性与香港参数误差之间的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound
Homodyned K-distribution (HK-distribution) parameter estimation in quantitative ultrasound (QUS) has been recently addressed using Bayesian Neural Networks (BNNs). BNNs have been shown to significantly reduce computational time in speckle statistics-based QUS without compromising accuracy and precision. Additionally, they provide estimates of feature uncertainty, which can guide the clinician's trust in the reported feature value. The total predictive uncertainty in Bayesian modeling can be decomposed into epistemic (uncertainty over the model parameters) and aleatoric (uncertainty inherent in the data) components. By decomposing the predictive uncertainty, we can gain insights into the factors contributing to the total uncertainty. In this study, we propose a method to compute epistemic and aleatoric uncertainties for HK-distribution parameters ($\alpha$ and $k$) estimated by a BNN, in both simulation and experimental data. In addition, we investigate the relationship between the prediction error and both uncertainties, shedding light on the interplay between these uncertainties and HK parameters errors.
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