Publication:
Few-shot learning for segmentation of yeast cell microscopy images

dc.contributor.coauthorAlkan, Muhammet
dc.contributor.coauthorKiraz, Berna
dc.contributor.coauthorEren, Furkan
dc.contributor.departmentDepartment of Physics
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorKiraz, Alper
dc.contributor.kuauthorUysallı, Yiğit
dc.contributor.schoolcollegeinstituteCollege of Sciences
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T23:58:03Z
dc.date.issued2021
dc.description.abstractCell segmentation from microscopic images can be performed using deep neural networks or image processing techniques. In addition to their inherent difficulties, these techniques come together with the requirement of feeding the neural network with a large number of image samples in order to obtain a good result. However, this is not sustainable in terms of collecting and labeling microscopic images and represents a costly and time-consuming solution for every new microscopic image and cell type. Instead, fine-tuning can be employed by taking advantage of the adaptation ability of a model trained using meta-learning algorithms. In this way, while more general and better results can be obtained with fewer samples, the training process does not start from scratch for each new cell type or data set. In this article, microscopic images of yeast cells were recorded and analyzed using Reptile algorithm. After fine-tuning with a small number of samples, an average success rate of 81 % IoU (Intersection over Union) was obtained on the test pictures in addition to the model accuracy reaching up to 87%.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.1109/SIU53274.2021.9477988
dc.identifier.isbn978-1-6654-3649-6
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85111430797
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9477988
dc.identifier.urihttps://hdl.handle.net/20.500.14288/15391
dc.identifier.wos808100700229
dc.keywordsYeast cell segmentation
dc.keywordsMeta learning
dc.keywordsReptile
dc.language.isotur
dc.publisherIEEE
dc.relation.ispartof29th IEEE Conference on Signal Processing and Communications Applications (Siu 2021)
dc.subjectCivil engineering
dc.subjectElectrical electronics engineering
dc.subjectTelecommunication
dc.titleFew-shot learning for segmentation of yeast cell microscopy images
dc.title.alternativeAz örnekli öǧrenme ile mikroskobik görüntülerden maya hücresi segmentasyonu
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorUysallı, Yiğit
local.contributor.kuauthorKiraz, Alper
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Sciences
local.publication.orgunit2Department of Physics
local.publication.orgunit2Graduate School of Sciences and Engineering
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