Learn from one data set to classify all - A multi-target domain adaptation approach for white blood cell classification

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dc.authoridATILA, UMIT/0000-0002-1576-9977
dc.authoridElen, Abdullah/0000-0003-1644-0476
dc.authoridBaydilli, Yusuf Yargi/0000-0002-4457-2081
dc.contributor.authorBaydilli, Yusuf Yargi
dc.contributor.authorAtila, Umit
dc.contributor.authorElen, Abdullah
dc.date.accessioned2024-09-29T15:55:09Z
dc.date.available2024-09-29T15:55:09Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBackground and objective: Traditional machine learning methods assume that both training and test data come from the same distribution. In this way, it becomes possible to achieve high successes when modelling on the same domain. Unfortunately, in real-world problems, direct transfer between domains is adversely affected due to differences in the data collection process and the internal dynamics of the data. In order to cope with such drawbacks, researchers use a method called domain adaptation, which enables the successful transfer of information learned in one domain to other domains. In this study, a model that can be used in the classification of white blood cells (WBC) and is not affected by domain differences was proposed. Methods: Only one data set was used as source domain, and an adaptation process was created that made possible the learned knowledge to be used effectively in other domains (multi-target domain adaptation). While constructing the model, we employed data augmentation, data generation and fine-tuning processes, respectively. Results: The proposed model has been able to extract domain-invariant features and achieved high success rates in the tests performed on nine different data sets. Multi-target domain adaptation accuracy was measured as %98.09. Conclusions: At the end of the study, it has been observed that the proposed model ignores the domain differences and it can adapt in a successful way to target domains. In this way, it becomes possible to classify unlabeled samples rapidly by using only a few number of labeled ones. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2020.105645
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid32702574en_US
dc.identifier.scopus2-s2.0-85088100336en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2020.105645
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4500
dc.identifier.volume196en_US
dc.identifier.wosWOS:000580609200056en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMedical data analysisen_US
dc.subjectWhite blood cells (WBC)en_US
dc.subjectDeep learningen_US
dc.subjectMulti-target domain adaptationen_US
dc.subjectClassificationen_US
dc.titleLearn from one data set to classify all - A multi-target domain adaptation approach for white blood cell classificationen_US
dc.typeArticleen_US

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