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Found 8 papers, 1 papers with code
Phase Aberration Correction: A Deep Learning-Based Aberration to Aberration Approach
This dataset serves to mitigate the data scarcity problem in the development of deep learning-based techniques for phase aberration correction.
Robust RF Data Normalization for Deep Learning
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 growing interest in ultrasound image processing.
Frequency-Space Prediction Filtering for Phase Aberration Correction in Plane-Wave Ultrasound
In this study, we illustrate the challenge of applying this technique to plane-wave imaging, where, at shallower depths, signals from more distant elements lose relevance, and a fewer number of elements contribute to image reconstruction.
Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach
Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium.
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
We also show that the network fine-tuned by our proposed method using experimental phantom data performs well on in vivo data similar to the network fine-tuned on in vivo data.
Ultrasound Domain Adaptation Using Frequency Domain Analysis
A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data.
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images
Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community.
Investigating Shift-Variance of Convolutional Neural Networks in Ultrasound Image Segmentation
To the best of our knowledge, this problem has not been studied in ultrasound image segmentation or even more broadly in ultrasound images.
