Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks

• URL: http://arxiv.org/abs/2211.00175v1
• Date: Mon, 31 Oct 2022 22:38:33 GMT
• Title: Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks
• Authors: Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, and Hassan Rivaz
• Abstract summary: The parameters of Homodyned K-distribution (HK-distribution) are the speckle statistics that can model the envelope data in diverse scattering conditions. We propose a Bayesian Neural Network (BNN) to estimate the parameters of HK-distribution and quantify the uncertainty of the estimator.
• Score: 2.599882743586164
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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