2k citations · 2.9k across the 8 of their papers we have counts for
9 papers
Automated triage of COVID-19 from various lung abnormalities using chest CT features
Dor Amran, Maayan Frid-Adar, Nimrod Sagie +3
The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 su…
COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
Rula Amer, Maayan Frid-Adar, Ophir Gozes +2
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning…
Coronavirus Detection and Analysis on Chest CT with Deep Learning
Ophir Gozes, Maayan Frid-Adar, Nimrod Sagie +3
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed c…
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
Ophir Gozes, Maayan Frid-Adar, Hayit Greenspan +5
Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from…
Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
Maayan Frid-Adar, Rula Amer, Hayit Greenspan
Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET)…
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Maayan Frid-Adar, Avi Ben-Cohen, Rula Amer +1
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutiona…
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using lar…
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
Maayan Frid-Adar, Eyal Klang, Michal Amitai +2
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first u…
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability w…