COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

@article{Aboutalebi2021COVIDNetCD,
  title={COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images},
  author={Hossein Aboutalebi and Maya Pavlova and Mohammad Javad Shafiee and Ali Sabri and Amer Alaref and Alexander Wong},
  journal={Diagnostics},
  year={2021},
  volume={12}
}
The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often… 

Figures and Tables from this paper

CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

TLDR
This research proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis without taking a huge computational time during the training process of the network.

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics

TLDR
Here, comprehensive details on the various aspects of the proposed COVIDx CXR-3 are provided including patient demographics, imaging views, and infection types to assist scientists inAdvancing computer vision research against the COVID-19 pandemic.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform

TLDR
A multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning is proposed and it yields the best performance compared to other state-of-the-art methods.

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

TLDR
The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

TLDR
An insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19 and the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace are discussed.

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

TLDR
Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages.

COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images

TLDR
COVID-Net CXR-2 is introduced, an enhanced deep convolutional neural network design for COVID-19 detection from CxR images built using a greater quantity and diversity of patients than the original CO VID-Net, and explainedability-driven performance validation was used to gain deeper insights in its decision-making behavior.

References

SHOWING 1-10 OF 62 REFERENCES

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

TLDR
An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric.

Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

TLDR
This study explores a more machine-centric strategy for quantifying the performance of explainability methods on deep neural networks via the notion of decision-making impact analysis and introduces two quantitative performance metrics: Impact Score, which assesses the percentage of critical factors with either strong confidence reduction impact or decision changing impact, and Impact Coverage, which assessing the percentage coverage of adversarially impacted factors in the input.

Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS

TLDR
The RALE score can be used to assess both the extent of pulmonary oedema and the severity of ARDS, by utilising information that is already obtained routinely, safely and inexpensively in every patient with ARDS.

Deep Residual Learning for Image Recognition

TLDR
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases From Chest X-Ray Images

TLDR
The proposed TB-Net not only achieves high tuberculosis case detection performance in terms of sensitivity and specificity, but also leverages clinically relevant critical factors in its decision making process.

COVIDx-US - An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics

TLDR
COVIDx-US is the first and largest open-access fully-curated benchmark lung ultrasound imaging dataset that contains a standardized and unified lung ultrasound score per video file, providing better interpretation while enabling other research avenues such as severity assessment.

COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning

TLDR
Enhanced deep neural networks for COVID-19 detection from chest CT images which are trained using a large, diverse, multinational patient cohort are introduced and suggest the strong potential ofDeep neural networks as an effective tool for computer-aided COVID,19 assessment.

The RSNA International COVID-19 Open Radiology Database (RICORD)

TLDR
The RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD), the first multi-institutional, multinational, expert-annotated CO VID-19 imaging data set, which is made freely available to the machine learning community.

Managing intensive care admissions when there are not enough beds during the COVID-19 pandemic: a systematic review

TLDR
A systematic review of current guidelines and the different approaches taken globally to manage the triage of intensive care resources during the COVID-19 pandemic developed a set of factors to consider when developing guidelines for managing intensive care admissions, and outlined implications for clinical leads and local implementation.

Deep learning based detection and analysis of COVID-19 on chest X-ray images

TLDR
The PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients are taken and deep learning-based CNN models are used, which give the highest accuracy for detecting Chest X-rays images as compared to other models.
...