3 citations · 5 across the 5 of their papers we have counts for
5 papers
The Art of the Steal: Purloining Deep Learning Models Developed for an Ultrasound Scanner to a Competitor Machine
Ufuk Soylu, Varun Chandrasekeran, Michael L. Oelze
A transfer function approach has recently proven effective for calibrating deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquis…
Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound
Ufuk Soylu, Michael L. Oelze
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…
Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions
Ufuk Soylu, Michael L. Oelze
Deep learning (DL) can fail when there are data mismatches between training and testing data. Due to its operator-dependent nature, acquisition-related data mismatches, caused by d…
A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training
Ufuk Soylu, Michael L. Oelze
Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultras…
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering
Ufuk Soylu, Yoram Bresler
Ultrasound Localization Microscopy (ULM) offers a cost-effective modality for microvascular imaging by using intravascular contrast agents (microbubbles). However, ULM has a fundam…