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Segmentation of Cellular Structures with Encoder-Decoder Based Deep Learning Algorithm in Histopathological Images

dc.contributor.authorAlbayrak, Abdulkadir
dc.contributor.authorBilgin, Gokhan
dc.date.accessioned2026-06-27T14:20:44Z
dc.date.issued2018
dc.description.abstractIn this proposed study, SegNet is used for segmentation of cellular structures in high-resolution histopathological images. SegNet is a deep convolutional encoder-decoder architecture used for segmenting objects on the road and indoors. In this proposed study, the segmentation performance of SegNet algorithm for cellular structures in high resolution histopathological images is tried to be obtained. We also compared the performance of the SegNet algorithm with the state-of-the-art segmentation algorithms in the literature. According to the obtained results, SegNet has been observed to be quite successful compared to other methods commonly used in the segmentation of cellular structures.en
dc.identifier.isbn978-1-5386-6852-8
dc.identifier.urihttps://hdl.handle.net/20.500.14981/59511
dc.identifier.wos000467637600036
dc.language.isotur
dc.publisherIEEE
dc.relation.conferenceMedical Technologies National Congress (TIPTEKNO)
dc.relation.ispartof2018 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO)
dc.subjectSemantic segmentation
dc.subjecthistopathological images
dc.subjectdeep learning
dc.subjectsegnet
dc.subjectnuclei segmentation
dc.subjectEngineering
dc.subjectMedical Informatics
dc.subjectMedical Laboratory Technology
dc.titleSegmentation of Cellular Structures with Encoder-Decoder Based Deep Learning Algorithm in Histopathological Images
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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