Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets (2020-04-13T00:00:00.000000Z)

TL;DR

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.

Abstract

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

Authors

J. C. Ye

8 papers

Y. Oh

1 papers

Sangjoon Park

1 papers

TL;DR

Abstract

Authors

References33 items

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases

COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

COVID-19 Image Data Collection

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19

Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients.

Chest Radiographic and CT Findings of the 2019 Novel Coronavirus Disease (COVID-19): Analysis of Nine Patients Treated in Korea

Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR

Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients

A review on lung boundary detection in chest X-rays

Public Datasets and Techniques for Segmentation of Anatomical Structures from Chest X-Rays: Comparitive Study, Current Trends and Future Directions

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Automatic differentiation in PyTorch

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

U-Net: Convolutional Networks for Biomedical Image Segmentation

Adam: A Method for Stochastic Optimization

Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

Cardiothoracic ratio from postero-anterior chest radiographs: a simple, reproducible and independent marker of disease severity and outcome in adults with congenital heart disease.

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database

Radiologic pattern of disease in patients with severe acute respiratory syndrome: the Toronto experience.

Community-acquired pneumonia

Automatic Differentiation: Techniques and Applications

THE CARDIOTHORACIC RATIO: AN INDEX OF CARDIAC ENLARGEMENT

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images

Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

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