X-ray Image Segmentation using Multi-task Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sejin | - |
dc.contributor.author | Jeong, Woojin | - |
dc.contributor.author | Moon, Young Shik | - |
dc.date.accessioned | 2021-06-22T09:06:41Z | - |
dc.date.available | 2021-06-22T09:06:41Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.issn | 1976-7277 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1208 | - |
dc.description.abstract | The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국인터넷정보학회 | - |
dc.title | X-ray Image Segmentation using Multi-task Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Moon, Young Shik | - |
dc.identifier.doi | 10.3837/tiis.2020.03.011 | - |
dc.identifier.scopusid | 2-s2.0-85082834877 | - |
dc.identifier.wosid | 000523207200011 | - |
dc.identifier.bibliographicCitation | KSII Transactions on Internet and Information Systems, v.14, no.3, pp.1104 - 1120 | - |
dc.relation.isPartOf | KSII Transactions on Internet and Information Systems | - |
dc.citation.title | KSII Transactions on Internet and Information Systems | - |
dc.citation.volume | 14 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1104 | - |
dc.citation.endPage | 1120 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002575336 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | COMPUTER-AIDED DETECTION | - |
dc.subject.keywordPlus | CHEST RADIOGRAPHS | - |
dc.subject.keywordPlus | LUNG NODULES | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordAuthor | image segmentation | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | lung nodule segmentation | - |
dc.identifier.url | http://itiis.org/digital-library/23390 | - |
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