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Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound
Additionally, robust implementation inspired by Wiener filtering provided an effective method for transferring the domain from one machine to another machine, and it can succeed using just a single calibration view without the need for multiple independent calibration frames.
Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions
By using the setting transfer functions, which allowed a matching of the training and testing domains, we obtained mean accuracies of 95. 3%, 92. 99% and 99. 32%, respectively.
A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training
In this work, we develop a data-efficient deep learning training strategy, which we named \textit{Zone Training}.
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering
We believe that the proposed velocity filtering method has the potential to pave the way to clinical translation of ULM.
