About: Coronavirus Detection and Analysis on Chest CT with Deep Learning

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Coronavirus Detection and Analysis on Chest CT with Deep Learning
Creator	Ji, Wenbin Zhang, Huangqi Frid-Adar, Maayan Gozes, Ophir Greenspan, Hayit »more»
source	ArXiv
abstract	The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in%22Mainland China%22with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.
has issue date	2020-04-06(xsd:dateTime)
has license	arxiv
sha1sum (hex)	78da2965707fb249e628f802aa519798f4861da4
resource representing a document's title	Coronavirus Detection and Analysis on Chest CT with Deep Learning
resource representing a document's body	covid:78da2965707fb249e628f802aa519798f4861da4#body_text
is schema:about of	named entity 'QUANTIFY' named entity 'CONFIRMED' named entity 'DISEASE' named entity 'DEEP LEARNING' named entity 'RADIOLOGISTS' »more»

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