Demo code and Network’s Weights for “Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches”

Proposed Model Projection Autoencoder (A), Graphical representation of the denoising autoencoder (B), Bayesian Neural Network (C).

The demo code and network’s weights can be found here:

Github Link

The simulation test datasets are also available here:

Test Dataset



If you use this dataset, please cite the following publication:

IEEE link

Arxiv

@article{tehrani2024homodyned,
  title={Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches},
  author={Tehrani, Ali KZ and Cloutier, Guy and Tang, An and Rosado-Mendez, Ivan M and Rivaz, Hassan},
  journal={IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
  year={2024},
  publisher={IEEE}
}