Deep convolutional network for tumor bud detection

Simple item page
buir.advisorDemir, Ƈiğdem GĆ¼ndĆ¼z
dc.contributor.authorKoƧ, Soner
dc.date.accessioned2019-04-11T06:00:51Z
dc.date.available2019-04-11T06:00:51Z
dc.date.copyright2019-04
dc.date.issued2019-04
dc.date.submitted2019-04-10
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2019.en_US
dc.descriptionIncludes bibliographical references (leaves 48-55).en_US
dc.description.abstractThe existence of tumor buds is accepted as a promising biomarker for staging colorectal carcinomas. In the current practice of medicine, these tumor buds are detected by the manual examination of a immunohistochemically (IHC) stained tissue sample under a microscope. This manual examination is time-consuming as well as it may lead to inter-observer variability. In order to obtain fast and reproducible examinations, developing computational solutions has been becoming more and more important. With this motivation, this thesis presents a fully convolutional network design for the purpose of automatic tumor bud detection, for the rst time. This network design extends the U-net architecture by considering up-to-date learning mechanisms. These mechanisms include using residual connections in the encoder path, employing both ELU and ReLU activation functions in di erent layers of the network, training the network with a Tversky loss function, and combining outputs of di erent layers of the decoder path to reconstruct the nal segmentation map. Our experiments on 3295 image tiles taken from 23 whole slide images of IHC stained colorectal carcinomatous samples show that this extended version helps alleviate the vanishing gradient problem and those related with having a high class-imbalance dataset. And as a result, this network design yields better segmentation results compared with those of the two state-of-the-art networks.en_US
dc.description.provenanceSubmitted by BetĆ¼l Ɩzen (ozen@bilkent.edu.tr) on 2019-04-11T06:00:51Z No. of bitstreams: 1 10243839.pdf: 13179863 bytes, checksum: c3c6337e17ce526ea16fe0ccdf35e9cb (MD5)en
dc.description.provenanceMade available in DSpace on 2019-04-11T06:00:51Z (GMT). No. of bitstreams: 1 10243839.pdf: 13179863 bytes, checksum: c3c6337e17ce526ea16fe0ccdf35e9cb (MD5) Previous issue date: 2019-04en
dc.description.statementofresponsibilityby Soner KoƧ.en_US
dc.format.extentx, 55 leaves : illustrations (some color), charts ; 30 cm.en_US
dc.identifier.itemidB160004
dc.identifier.urihttp://hdl.handle.net/11693/50818
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectFully convolutional networksen_US
dc.subjectDigital pathologyen_US
dc.subjectTumor buddingen_US
dc.subjectColorectal carcinomasen_US
dc.titleDeep convolutional network for tumor bud detectionen_US
dc.title.alternativeTĆ¼mƶr tomurcuklanma tespiti iƧin derin evrişimsel ağen_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
10243839.pdf
Size:
12.57 MB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Graduate School of Engineering and Science