About: Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study
Creator	Li, Hongjun Yang, Xin Wang, Li Li, Liang Li, Yang Li, Li Niu, Meng He, Bingxi Hui, Hui Li, L Tian, Jie Zha, Yunfei Li, Niu Tian, Wu, Xiangjun Zha,
source	Elsevier; Medline; PMC
abstract	PURPOSE: To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. METHODS: We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets. RESULTS: The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively. CONCLUSIONS: Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.
has issue date	2020-05-05(xsd:dateTime)
bibo:doi	10.1016/j.ejrad.2020.109041
bibo:pmid	32408222
has license	no-cc
sha1sum (hex)	e8b9eaa883485ed777d2575608c44e92b0c106cc
schema:url	https://doi.org/10.1016/j.ejrad.2020.109041
resource representing a document's title	Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study
has PubMed Central identifier	PMC7198437
has PubMed identifier	32408222
schema:publication	Eur J Radiol
resource representing a document's body	covid:e8b9eaa883485ed777d2575608c44e92b0c106cc#body_text
is schema:about of	named entity 'COVID-19' named entity 'Deep learning' named entity 'Tian' named entity 'Zha' covid:arg/e8b9eaa883485ed777d2575608c44e92b0c106cc named entity 'COVID-19' named entity 'chest CT' named entity 'COVID-19' named entity 'deep learning network' named entity 'tumour' named entity 'COVID' named entity 'bacterial pneumonia' named entity 'good performance' named entity 'pneumonia' named entity 'overfitting' named entity 'training set' named entity 'view model' named entity 'COVID' named entity 'morphological' named entity 'pneumonia' named entity 'GE Medical' named entity 'COVID' named entity 'COVID' named entity 'COVID' named entity 'reverse-transcriptase' named entity 'pneumonia' named entity 'GE Medical' named entity 'pneumonia' named entity 'mediastinal' named entity 'radiologist' named entity 'CT image' named entity 'China' named entity 'COVID' named entity 'lung' named entity 'COVID' named entity 'false negative' named entity 'deep learning-based' named entity 'deep network' named entity 'AI models' named entity 'lung' named entity 'true positive' named entity 'decision-making' named entity 'radiologists' named entity 'lung' named entity 'subgroup analysis' named entity 'deep learning' named entity 'GGO' named entity 'infection' named entity 'true positive' named entity 'view model' named entity 'COVID' named entity 'chest CT' named entity 'gene mutations' named entity 'Mann-Whitney U test' named entity 'radiologists' named entity 'training set' named entity 'ROC curve' named entity 'image segmentation' named entity 'China' named entity 'deep learning' named entity 'deep learning-based' named entity 'validation set' named entity 'pneumonia' named entity 'COVID-19' named entity 'lung' named entity 'Python'

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