  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.

Signal Processing;Machine Learning;Image and Video Processing