Learned super resolution ultrasound for improved breast lesion characterization

no code implementations • 12 Jul 2021 • Or Bar-Shira, Ahuva Grubstein, Yael Rapson, Dror Suhami, Eli Atar, Keren Peri-Hanania, Ronnie Rosen, Yonina C. Eldar

This study demonstrates the feasibility of in vivo human super resolution, based on a clinical scanner, to increase US specificity for different breast lesions and promotes the use of US in the diagnosis of breast pathologies.

Specificity Super-Resolution

Point of Care Image Analysis for COVID-19

no code implementations • 28 Oct 2020 • Daniel Yaron, Daphna Keidar, Elisha Goldstein, Yair Shachar, Ayelet Blass, Oz Frank, Nir Schipper, Nogah Shabshin, Ahuva Grubstein, Dror Suhami, Naama R. Bogot, Eyal Sela, Amiel A. Dror, Mordehay Vaturi, Federico Mento, Elena Torri, Riccardo Inchingolo, Andrea Smargiassi, Gino Soldati, Tiziano Perrone, Libertario Demi, Meirav Galun, Shai Bagon, Yishai M. Elyada, Yonina C. Eldar

Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19.

COVID-19 Classification of X-ray Images Using Deep Neural Networks

no code implementations • 3 Oct 2020 • Elisha Goldstein, Daphna Keidar, Daniel Yaron, Yair Shachar, Ayelet Blass, Leonid Charbinsky, Israel Aharony, Liza Lifshitz, Dimitri Lumelsky, Ziv Neeman, Matti Mizrachi, Majd Hajouj, Nethanel Eizenbach, Eyal Sela, Chedva S Weiss, Philip Levin, Ofer Benjaminov, Gil N Bachar, Shlomit Tamir, Yael Rapson, Dror Suhami, Amiel A Dror, Naama R Bogot, Ahuva Grubstein, Nogah Shabshin, Yishai M Elyada, Yonina C Eldar

The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.

Data Augmentation General Classification