Pyramidal position attention model for histopathological image segmentation

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dc.authoridBOZDAĞ KARAKEÇİ, Zehra/0000-0002-1119-5275
dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authorwosidBOZDAĞ KARAKEÇİ, Zehra/AAM-8820-2021
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.contributor.authorBozdag, Zehra
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:53:08Z
dc.date.available2024-08-04T20:53:08Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe level of performance achieved in the classification of histopathological images has not yet been reached in the segmentation area. This is because the global context information sufficient for classification is not sufficient for segmentation. Especially, high tissue diversity in histopathological images and the fact that tissues in the same class have quite different colors, patterns and geometries make the segmentation problem difficult. In this study, a novel hybrid architecture (PAMSegNet) is presented that provides high segmentation accuracy in histopathological images. This architecture, which has a pyramid data processing strategy, has been provided with the Position Attention Module (PAM) and Boundary aware Module (BM) to extract global and local attri-butes more accurately. In addition, with the deep supervised technique used, both contents (global and local) were evaluated together in the segmentation decision. Segmentation architectures (Deeplabv3 +, SegNet, U-Net) with a strong backbone in the literature are used for performance comparison. The proposed architecture has been found to provide high segmentation accuracy (71.6% mIoU and 86.4% PA).en_US
dc.identifier.doi10.1016/j.bspc.2022.104374
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85141913109en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.104374
dc.identifier.urihttps://hdl.handle.net/11616/100992
dc.identifier.volume80en_US
dc.identifier.wosWOS:000890727300003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectHistopathological image segmentationen_US
dc.subjectAttention moduleen_US
dc.subjectConvolution neural networken_US
dc.titlePyramidal position attention model for histopathological image segmentationen_US
dc.typeArticleen_US

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