Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound

TL;DR

This paper introduces a method to decompose and analyze the uncertainty components in Bayesian neural network estimates of Homodyned K-distribution parameters in quantitative ultrasound, enhancing understanding of prediction errors.

Contribution

It proposes a novel approach to compute and analyze epistemic and aleatoric uncertainties in HK-distribution parameter estimation using BNNs, applicable to both simulated and experimental data.

Findings

Uncertainty decomposition reveals the sources of prediction errors.

Epistemic and aleatoric uncertainties correlate with estimation accuracy.

The method improves confidence in ultrasound feature estimation.

Abstract

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.