289 citations · 1.7k across the 90 of their papers we have counts for
8 papers · 2 filters
Self-supervised Learning of Dense Shape Correspondence
Oshri Halimi, Or Litany, Emanuele Rodolà +2
We introduce the first completely unsupervised correspondence learning approach for deformable 3D shapes. Key to our model is the understanding that natural deformations (such as c…
Learning beamforming in ultrasound imaging
Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov +3
Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. In this paper, we demo…
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Chaim Baskin, Natan Liss, Yoav Chai +5
Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning…
High frame-rate cardiac ultrasound imaging with deep learning
Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov +5
Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received…
High quality ultrasonic multi-line transmission through deep learning
Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov +5
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography. Several methods have been proposed in the medical ultrasound literature aiming at acceleratin…
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
Tal Remez, Or Litany, Raja Giryes +1
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising p…