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https://hdl.handle.net/20.500.13091/2910
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bayrak, Gıyaseddin | - |
dc.contributor.author | Toprak, M. Şakir | - |
dc.contributor.author | Ganiz, Murat Can | - |
dc.contributor.author | Kodaz, Halife | - |
dc.contributor.author | Koç, Ural | - |
dc.date.accessioned | 2022-10-08T20:48:57Z | - |
dc.date.available | 2022-10-08T20:48:57Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9781643682846 | - |
dc.identifier.issn | 0926-9630 | - |
dc.identifier.uri | https://doi.org/10.3233/SHTI220609 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/2910 | - |
dc.description | Norwegian Centre for E-health Research | en_US |
dc.description | 32nd Medical Informatics Europe Conference, MIE 2022 -- 27 May 2022 through 30 May 2022 -- 179490 | en_US |
dc.description.abstract | Radiology 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.iso | en | en_US |
dc.publisher | IOS Press BV | en_US |
dc.relation.ispartof | Studies in Health Technology and Informatics | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Brain Hemorrhage | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | NLP | en_US |
dc.subject | Radiology | en_US |
dc.subject | Computational linguistics | en_US |
dc.subject | Computerized tomography | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Medical informatics | en_US |
dc.subject | Natural language processing systems | en_US |
dc.subject | Radiation | en_US |
dc.subject | Brain hemorrhage | en_US |
dc.subject | Critical case | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Domain specific | en_US |
dc.subject | Fine tuning | en_US |
dc.subject | Haemorrage | en_US |
dc.subject | Hemorrhage detection | en_US |
dc.subject | Language model | en_US |
dc.subject | Learning classifiers | en_US |
dc.subject | Radiology reports | en_US |
dc.subject | Radiology | en_US |
dc.subject | brain hemorrhage | en_US |
dc.subject | human | en_US |
dc.subject | natural language processing | en_US |
dc.subject | research | en_US |
dc.subject | x-ray computed tomography | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Humans | en_US |
dc.subject | Intracranial Hemorrhages | en_US |
dc.subject | Natural Language Processing | en_US |
dc.subject | Research Report | en_US |
dc.subject | Tomography, X-Ray Computed | en_US |
dc.title | Deep Learning-Based Brain Hemorrhage Detection in CT Reports | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.3233/SHTI220609 | - |
dc.identifier.pmid | 35612228 | en_US |
dc.identifier.scopus | 2-s2.0-85131106697 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.identifier.volume | 294 | en_US |
dc.identifier.startpage | 866 | en_US |
dc.identifier.endpage | 867 | en_US |
dc.institutionauthor | Toprak, M. Şakir | - |
dc.institutionauthor | Kodaz, Halife | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57720752500 | - |
dc.authorscopusid | 57720752600 | - |
dc.authorscopusid | 22034124700 | - |
dc.authorscopusid | 8945093700 | - |
dc.authorscopusid | 57192644774 | - |
dc.identifier.scopusquality | Q3 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairetype | Conference Object | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.03. 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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File | Size | Format | |
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SHTI-294-SHTI220609.pdf | 150.83 kB | Adobe PDF | View/Open |
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