104 citations · 104 across the 2 of their papers we have counts for
6 papers
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…
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data
Ophir Gozes, Hayit Greenspan
In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accom…
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset
Ophir Gozes, Hayit Greenspan
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks…
Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training
Ophir Gozes, Hayit Greenspan
The abundance of overlapping anatomical structures appearing in chest radiographs can reduce the performance of lung pathology detection by automated algorithms (CAD) as well as th…