Pyramidal Nonlocal Network for Histopathological Image of Breast Lymph Node Segmentation

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dc.authoridBOZDAĞ KARAKEÇİ, Zehra/0000-0002-1119-5275
dc.authorwosidBOZDAĞ KARAKEÇİ, Zehra/AAM-8820-2021
dc.contributor.authorBozdag, Zehra
dc.contributor.authorTalu, Fatih M.
dc.date.accessioned2024-08-04T20:49:17Z
dc.date.available2024-08-04T20:49:17Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe convolutional neural networks (CNNs) are frequently used in the segmentation of histopathological whole slide image-(WSI) acquired breast lymph nodes. The first layers in deep network architectures generally encode the geometric and color properties of objects in the training set, while the last layers encode the distinctive and detailed properties between classes. Modern segmentation approaches (DeepLabV3+, SegNet, PSPNet) are realized by evaluating these layers together. However, having a high parameter space of all these networks increases the calculation costs and prevents the researchers from working more effectively. In this study, we present a new pyramid-structured segmentation network (NonLocalSeg). Although the proposed network has low parameter space, its segmentation performance is similar to current architectures. The integration of the Non-local Module (NLM-a form of attention mechanism) or Asymmetric Pyramid Nonlocal block (APNB) into classical pyramid-built architectures has led to the reduction of network depth and narrowing of the parameter space while enabling coding of low and high image features. These mechanisms suppressed the unfocused background image, emphasizing the focused foreground object. As a result of a series of ablation experiments carried out, it is seen that the NLM and APNL mechanisms give the succeeded results. Although the network architectures adapting these mechanisms contain fewer parameters than current networks, it is observed that they have a similar accuracy (mean intersection over union [IoU]) range. (C) 2021 The Authors. Published by Atlantis Press B.V.en_US
dc.identifier.doi10.2991/ijcis.d.201030.001
dc.identifier.endpage131en_US
dc.identifier.issn1875-6891
dc.identifier.issn1875-6883
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85100838582en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage122en_US
dc.identifier.urihttps://doi.org/10.2991/ijcis.d.201030.001
dc.identifier.urihttps://hdl.handle.net/11616/99765
dc.identifier.volume14en_US
dc.identifier.wosWOS:000617701700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringernatureen_US
dc.relation.ispartofInternational Journal of Computational Intelligence Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectHistopathological image segmentationen_US
dc.subjectNonlocal networken_US
dc.subjectMachine learningen_US
dc.titlePyramidal Nonlocal Network for Histopathological Image of Breast Lymph Node Segmentationen_US
dc.typeArticleen_US

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