Deep learning (DL) can fail when there are data mismatches between training and testing
data distributions. Due to its operator-dependent nature, acquisition-related data …

A transfer function approach was recently demonstrated to mitigate data mismatches at the
acquisition level for a single ultrasound scanner in deep learning (DL)-based quantitative …

A transfer function approach has recently proven effective for calibrating deep learning (DL)
algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquisition …

Radio frequency (RF) data contain richer information compared to other data types, such as
envelope or B-mode, and employing RF data for training deep neural networks has attracted …

The backscatter coefficient is a fundamental property of tissues, much like the attenuation
and sound speed. From the backscatter coefficient, different scatterer properties describing …

The thesis of this dissertation suggests that there is a continuous demand for the
development of data-efficient and robust deep learning (DL) algorithms in order to promote …

Ultrasound (US) imaging is increasingly attracting the attention of both academic and
industrial researchers due to being a real-time and nonionizing imaging modality. It is also …