Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2910
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dc.contributor.authorBayrak, Gıyaseddin-
dc.contributor.authorToprak, M. Şakir-
dc.contributor.authorGaniz, Murat Can-
dc.contributor.authorKodaz, Halife-
dc.contributor.authorKoç, Ural-
dc.date.accessioned2022-10-08T20:48:57Z-
dc.date.available2022-10-08T20:48:57Z-
dc.date.issued2022-
dc.identifier.isbn9781643682846-
dc.identifier.issn0926-9630-
dc.identifier.urihttps://doi.org/10.3233/SHTI220609-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/2910-
dc.descriptionNorwegian Centre for E-health Researchen_US
dc.description32nd Medical Informatics Europe Conference, MIE 2022 -- 27 May 2022 through 30 May 2022 -- 179490en_US
dc.description.abstractRadiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.en_US
dc.language.isoenen_US
dc.publisherIOS Press BVen_US
dc.relation.ispartofStudies in Health Technology and Informaticsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBrain Hemorrhageen_US
dc.subjectDeep Learningen_US
dc.subjectNLPen_US
dc.subjectRadiologyen_US
dc.subjectComputational linguisticsen_US
dc.subjectComputerized tomographyen_US
dc.subjectDeep learningen_US
dc.subjectMedical informaticsen_US
dc.subjectNatural language processing systemsen_US
dc.subjectRadiationen_US
dc.subjectBrain hemorrhageen_US
dc.subjectCritical caseen_US
dc.subjectDeep learningen_US
dc.subjectDomain specificen_US
dc.subjectFine tuningen_US
dc.subjectHaemorrageen_US
dc.subjectHemorrhage detectionen_US
dc.subjectLanguage modelen_US
dc.subjectLearning classifiersen_US
dc.subjectRadiology reportsen_US
dc.subjectRadiologyen_US
dc.subjectbrain hemorrhageen_US
dc.subjecthumanen_US
dc.subjectnatural language processingen_US
dc.subjectresearchen_US
dc.subjectx-ray computed tomographyen_US
dc.subjectDeep Learningen_US
dc.subjectHumansen_US
dc.subjectIntracranial Hemorrhagesen_US
dc.subjectNatural Language Processingen_US
dc.subjectResearch Reporten_US
dc.subjectTomography, X-Ray Computeden_US
dc.titleDeep Learning-Based Brain Hemorrhage Detection in CT Reportsen_US
dc.typeConference Objecten_US
dc.identifier.doi10.3233/SHTI220609-
dc.identifier.pmid35612228en_US
dc.identifier.scopus2-s2.0-85131106697en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume294en_US
dc.identifier.startpage866en_US
dc.identifier.endpage867en_US
dc.institutionauthorToprak, M. Şakir-
dc.institutionauthorKodaz, Halife-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57720752500-
dc.authorscopusid57720752600-
dc.authorscopusid22034124700-
dc.authorscopusid8945093700-
dc.authorscopusid57192644774-
dc.identifier.scopusqualityQ3-
item.openairetypeConference Object-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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