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Found 9 papers, 0 papers with code
Automated triage of COVID-19 from various lung abnormalities using chest CT features
The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread.
COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression.
Coronavirus Detection and Analysis on Chest CT with Deep Learning
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives.
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score.
Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room.
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks.
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Then we present a novel scheme for liver lesion classification using CNN.
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs).
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for researchers.
