About: Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia

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About: Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
Creator	Xia, Liming Wang, Qian Liu, Jun Yan, Fuhua Xia, L Shi, Feng Wei, Ying Shan, Fei Liu, J Wu, D Cao, X Gao, Y Yang, Q Yang, Qi Ding, Z Ding, Zhongxiang Gao, Yaozong Mo, Z Mo, Zhanhao Shen, D Shen, Dinggang Wei, Y Wu, Dijia Yan, F Yuan, H Yuan, Huan Cao, Xiaohuan Huo, J Huo, Jiayu Ouyang, Xi Ouyang, † Shan, F Shi, )
topic	covid:601c4188ef2a0155c9e894abdc86b77776ad3192#this
Source	ArXiv
abstract	The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.
has issue date	2020-05-06(xsd:dateTime)
has license	arxiv
sha1sum (hex)	601c4188ef2a0155c9e894abdc86b77776ad3192
resource representing a document's title	Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
resource representing a document's body	covid:601c4188ef2a0155c9e894abdc86b77776ad3192#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/601c4188ef2a0155c9e894abdc86b77776ad3192
is schema:about of	named entity 'Our' named entity 'stage' named entity 'hospitals' named entity '200' named entity 'SYMPTOM ONSET' named entity 'LARGEST' named entity 'KNOWLEDGE' named entity 'MAKING DECISIONS' named entity 'INFECTION' named entity 'PARTIALLY' named entity 'SPREADING' named entity 'HOSPITALS' named entity 'EVALUATED' named entity 'NOVEL' named entity 'disease' named entity 'COVID-19' named entity 'onset' named entity 'COVID-19' named entity 'testing' named entity 'attention' named entity 'rapidly' named entity 'early' named entity 'regions' named entity 'deep networks' named entity 'COVID' named entity 'radiographic' named entity 'lung' named entity 'COVID' named entity 'countries and territories' named entity 'CAP' named entity 'making' named entity 'exists' named entity 'convolutional' named entity 'symptom' named entity 'sampling' named entity 'COVID-19' named entity 'convolutional network' named entity 'CT scans' named entity 'COVID' named entity 'infection' named entity 'sampling' named entity 'chest CT' named entity 'CAP' named entity 'cross entropy' named entity 'cross-validation' named entity 'pneumonia' named entity 'receiver operating characteristic' named entity 'data sampling' named entity 'COVID' named entity 'Radiological Society of North America' named entity 'infection' named entity 'COVID' named entity 'COVID'

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