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dc.contributor.authorAlbaradei, Somayah
dc.contributor.authorNapolitano, Francesco
dc.contributor.authorUludag, Mahmut
dc.contributor.authorThafar, Maha
dc.contributor.authorNapolitano, Sara
dc.contributor.authorEssack, Magbubah
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorGao, Xin
dc.date.accessioned2020-09-09T07:41:45Z
dc.date.available2020-09-09T07:41:45Z
dc.date.issued2020
dc.identifier.citationAlbaradei, S., Napolitano, F., Uludag, M., Thafar, M., Napolitano, S., Essack, M., … Gao, X. (2020). Automated counting of colony forming units using deep transfer learning from a model for congested scenes analysis. IEEE Access, 1–1. doi:10.1109/access.2020.3021656
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2020.3021656
dc.identifier.urihttp://hdl.handle.net/10754/665031
dc.description.abstractReliable quantification of cellular treatment effects in many bioassays depends on the accuracy of cell colony counting. However, colony counting processes tend to be tedious, slow, and error-prone. Thus, pursuing an effective colony counting technique is ongoing, and varies from manual approaches to partly automated and fully automated techniques. Most fully automated techniques were developed using deep learning (DL). A significant problem in applying DL to this task is the lack of sizeable collections of annotated plate images. For this reason, here we propose an application of Transfer Learning to cell colony counting that can overcome this problem by exploiting models trained for other tasks. To demonstrate this idea’s feasibility, we show how a small dataset can be used to transform a DL model designed for counting objects in congested scenes into a specialized cell colony counting model and achieve better performance than existing, more widely-used models.
dc.description.sponsorshipThis study is supported by grants from King Abdullah University of Technology (KAUST), grants BAS/1/1606-01-01, FCC/1/1976-17-01, and BAS/1/1624-01-01.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9186026/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9186026
dc.subjectArtificial intelligence
dc.subjectcolony count
dc.subjectmachine learning
dc.subjecttransfer learning
dc.titleAutomated counting of colony forming units using deep transfer learning from a model for congested scenes analysis
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalIEEE Access
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionTelethon Institute of Genetics and Medicine; Pozzuoli, Naples, Italy.
dc.identifier.pages1-1
kaust.personAlbaradei, Somayah
kaust.personNapolitano, Francesco
kaust.personUludag, Mahmut
kaust.personThafar, Maha
kaust.personEssack, Magbubah
kaust.personBajic, Vladimir B.
kaust.personGao, Xin
kaust.grant.numberBAS/1/1606-01-01
kaust.grant.numberBAS/1/1624-01-01
kaust.grant.numberFCC/1/1976-17-01
dc.relation.issupplementedbygithub:SomayahAlbaradei/tlcc
refterms.dateFOA2020-09-09T07:42:17Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: SomayahAlbaradei/tlcc: Transfer Learning Colony Count. Publication Date: 2019-10-22. github: <a href="https://github.com/SomayahAlbaradei/tlcc" >SomayahAlbaradei/tlcc</a> Handle: <a href="http://hdl.handle.net/10754/667896" >10754/667896</a></a></li></ul>


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