אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. At the same time, there are trade-offs among different US imaging qualities such as frame-rate, resolution, signal-to-noise-ratio and contrast. So far, these trade-offs have been compensated by mostly traditional model-based signal-processing methods. In the wake of the recent revival of artificial neural networks (NN), or more specifically, deep convolutional neural networks (CNN) for different tasks including image and signal processing, we present in this work a step towards replacing the traditional ultrasound processing pipeline with a data-driven, learnable one.

* *MSc seminar under supervision of Prof. Alex Bronstein and Prof. Michael Zibulevsky.