DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Yujin | ko |
dc.contributor.author | Park, Sangjoon | ko |
dc.contributor.author | Ye, Jong Chul | ko |
dc.date.accessioned | 2020-08-21T07:55:09Z | - |
dc.date.available | 2020-08-21T07:55:09Z | - |
dc.date.created | 2020-08-19 | - |
dc.date.created | 2020-08-19 | - |
dc.date.created | 2020-08-19 | - |
dc.date.issued | 2020-08 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON MEDICAL IMAGING, v.39, no.8, pp.2688 - 2700 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | http://hdl.handle.net/10203/275915 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets | - |
dc.type | Article | - |
dc.identifier.wosid | 000554893500012 | - |
dc.identifier.scopusid | 2-s2.0-85087516571 | - |
dc.type.rims | ART | - |
dc.citation.volume | 39 | - |
dc.citation.issue | 8 | - |
dc.citation.beginningpage | 2688 | - |
dc.citation.endingpage | 2700 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.identifier.doi | 10.1109/TMI.2020.2993291 | - |
dc.contributor.localauthor | Ye, Jong Chul | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Lung | - |
dc.subject.keywordAuthor | Diseases | - |
dc.subject.keywordAuthor | Image segmentation | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Sensitivity | - |
dc.subject.keywordAuthor | Computed tomography | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | chest X-ray | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | segmentation | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | saliency map | - |
dc.subject.keywordPlus | CHEST RADIOGRAPHS | - |
dc.subject.keywordPlus | CARDIOTHORACIC RATIO | - |
dc.subject.keywordPlus | DISEASE | - |
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