A data-efficient deep learning strategy for tissue characterization via quantitative ultrasound: Zone training
Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field
where researchers adapt the image analysis capabilities of DL algorithms to biomedical …
where researchers adapt the image analysis capabilities of DL algorithms to biomedical …
Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound
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 …
acquisition level for a single ultrasound scanner in deep learning (DL)-based quantitative …
Calibrating data mismatches in deep learning-based quantitative ultrasound using setting transfer functions
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 …
data distributions. Due to its operator-dependent nature, acquisition-related data …
Machine learning-enabled quantitative ultrasound techniques for tissue differentiation
Purpose Quantitative ultrasound (QUS) infers properties about tissue microstructure from
backscattered radio-frequency ultrasound data. This paper describes how to implement the …
backscattered radio-frequency ultrasound data. This paper describes how to implement the …
Deep learning in medical ultrasound—from image formation to image analysis
Over the past years, deep learning has established itself as a powerful tool across a broad
spectrum of domains. While deep neural networks initially found nurture in the computer …
spectrum of domains. While deep neural networks initially found nurture in the computer …
Sensor geometry generalization to untrained conditions in quantitative ultrasound imaging
Recent improvements in deep learning have brought great progress in ultrasonic lesion
quantification. However, the learning-based scheme performs properly only when a certain …
quantification. However, the learning-based scheme performs properly only when a certain …
A survey of deep-learning applications in ultrasound: Artificial intelligence–powered ultrasound for improving clinical workflow
Ultrasound is the most commonly used imaging modality in clinical practice because it is a
nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time …
nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time …
Coupling speckle noise suppression with image classification for deep-learning-aided ultrasound diagnosis
R Wang, X Liu, G Tan - Physics in Medicine & Biology, 2024 - iopscience.iop.org
Objective. During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image
classification is a foundational task. Due to the existence of serious speckle noise in US …
classification is a foundational task. Due to the existence of serious speckle noise in US …
Deep learning to obtain simultaneous image and segmentation outputs from a single input of raw ultrasound channel data
Single plane wave transmissions are promising for automated imaging tasks requiring high
ultrasound frame rates over an extended field of view. However, a single plane wave …
ultrasound frame rates over an extended field of view. However, a single plane wave …
Benchmarking deep learning models for automatic ultrasonic imaging inspection
J Ye, N Toyama - IEEE Access, 2021 - ieeexplore.ieee.org
The success of deep neural networks in carrying out a wide variety of cognitive tasks also
raised expectations regarding the advent of AI for the ultrasonic testing (UT) data …
raised expectations regarding the advent of AI for the ultrasonic testing (UT) data …