• Corpus ID: 232076291

CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays

@article{Abdulah2021CXRNetAA,
  title={CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays},
  author={Haikal Abdulah and Benjamin Huber and Sinan Lal and Hassan Abdallah and Luigi Leonardo Palese and Hamid Soltanian-Zadeh and Domenico L. Gatti},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.00087}
}
CXR-Net is a two-module Artificial Intelligence pipeline for the quick detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 was trained on a public dataset of 6395 CXRs with radiologist annotated lung contours to generate masks of the lungs that overlap the heart and large vasa. Module 2 is a hybrid convnet in which the first convolutional layer with learned coefficients is replaced by a layer with fixed coefficients provided by the Wavelet Scattering Transform (WST). Module 2 takes as… 
A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis
g-CXR-Net: A Graphic Application for the Rapid Recognition of SARS-CoV-2 from Chest X-Rays
g-CXR-Net is a graphic application for the rapid recognition of SARS-CoV-2 from Antero/Posterior chest X-rays. It employs the Artificial Intelligence engine of CXR-Net (arXiv:2103.00087) to generate

References

SHOWING 1-10 OF 50 REFERENCES
Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images
TLDR
Res-CR-Net, a new type of fully convolutional neural network that does not adopt a U-Net architecture, excels at segmentation tasks traditionally considered very hard, like recognizing the contours of nuclei, cytoplasm and mitochondria in densely packed cells in either EM or LM/FM images.
Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review
TLDR
Pulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT, suggesting that CT is a more sensitive imaging modality for investigation.
Scattering Networks for Hybrid Representation Learning
TLDR
It is demonstrated that the early layers of CNNs do not necessarily need to be learned, and can be replaced with a scattering network instead, and using hybrid architectures, this fact is used to train hybrid GANs to generate images.
Scaling the Scattering Transform: Deep Hybrid Networks
We use the scattering network as a generic and fixed initialization of the first layers of a supervised hybrid deep network. We show that early layers do not necessarily need to be learned, providing
Deep Learning with Python. Shelter Island, NY 11964
  • 2018
DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set
TLDR
DeepCOVID-XR, an artificial intelligence algorithm, detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experienced thoracic radiologists in consensus.
Lung Segmentation from Chest X-rays using Variational Data Imputation
TLDR
This work treats the high opacity regions of lungs from such abnormal CXRs as missing data and presents a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation.
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
TLDR
Experimental results show that the proposed patch-based convolutional neural network approach achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
COVID-19 Image Data Collection
TLDR
The initial COVID-19 open image data collection was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
TLDR
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
...
...