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    Automated counting of colony forming units using deep transfer learning from a model for congested scenes analysis

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    Automated.pdf
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    Type
    Article
    Authors
    Albaradei, Somayah cc
    Napolitano, Francesco
    Uludag, Mahmut cc
    Thafar, Maha A. cc
    Napolitano, Sara
    Essack, Magbubah cc
    Bajic, Vladimir B. cc
    Gao, Xin cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    KAUST Grant Number
    BAS/1/1606-01-01
    BAS/1/1624-01-01
    FCC/1/1976-17-01
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/665031
    
    Metadata
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    Abstract
    Reliable 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.
    Citation
    Albaradei, 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
    Sponsors
    This 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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Access
    DOI
    10.1109/ACCESS.2020.3021656
    Additional Links
    https://ieeexplore.ieee.org/document/9186026/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9186026
    Relations
    Is Supplemented By:
    • [Software]
      Title: SomayahAlbaradei/tlcc: Transfer Learning Colony Count. Publication Date: 2019-10-22. github: SomayahAlbaradei/tlcc Handle: 10754/667896
    ae974a485f413a2113503eed53cd6c53
    10.1109/ACCESS.2020.3021656
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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