Automated counting of colony forming units using deep transfer learning from a model for congested scenes analysis
Type
ArticleAuthors
Albaradei, Somayah
Napolitano, Francesco
Uludag, Mahmut

Thafar, Maha A.

Napolitano, Sara
Essack, Magbubah

Bajic, Vladimir B.

Gao, Xin

KAUST Department
Applied Mathematics and Computational Science ProgramComputational 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/1/1606-01-01BAS/1/1624-01-01
FCC/1/1976-17-01
Date
2020Permanent link to this record
http://hdl.handle.net/10754/665031
Metadata
Show full item recordAbstract
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.2020.3021656Sponsors
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.Journal
IEEE AccessAdditional 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