Integration of attention mechanisms into segmentation architectures and their application on breast lymph node images

Basit Öğe Kaydı
dc.contributor.authorYazan, Ersan
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:11:42Z
dc.date.available2024-08-04T20:11:42Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractInnovations such as the widespread use of motorized microscopes, the automatic scanning of the tissue taken from the patient and transferring it to a single large image, and the production of deep/adversarial networks specific to segmentation have increased the hope of automatically producing outputs very close to expert labeling in the segmentation problem. Particularly, it is known that segmentation performances are improved by integrating attention modules into classical 3D-UNet or GAN architectures. In this study, the effects of four different attention modules (DAF, DAF3D, DANet and MSA) were analyzed in solving the histopathological image segmentation problem. While single (SLF) and multiple (MLF) layer features are used together in DAF and DAF3D modules, two different mechanisms, position attention module and channel attention module, are used in DANet and MSA modules. As a result of the experimental studies, it has been seen that the DAF3D attention module maximizes the segmentation accuracy (0.76 mIoU and 0.89 PA). At the same time, the method with the lowest segmentation cost (0.156 seconds for 1 image) among the approaches was again DAF3D.en_US
dc.identifier.doi10.5505/pajes.2022.07838
dc.identifier.endpage255en_US
dc.identifier.issn1300-7009
dc.identifier.issn2147-5881
dc.identifier.issue3en_US
dc.identifier.startpage248en_US
dc.identifier.trdizinid1188624en_US
dc.identifier.urihttps://doi.org/10.5505/pajes.2022.07838
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1188624
dc.identifier.urihttps://hdl.handle.net/11616/92937
dc.identifier.volume29en_US
dc.identifier.wosWOS:001018927800004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherPamukkale Univen_US
dc.relation.ispartofPamukkale University Journal of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAttention mechanismen_US
dc.subjectHistopathologyen_US
dc.subjectSegmentationen_US
dc.subjectDANeten_US
dc.subjectDAFen_US
dc.subjectDAF3Den_US
dc.titleIntegration of attention mechanisms into segmentation architectures and their application on breast lymph node imagesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu