Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review
Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling h...
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Wiley
2021
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oai:doaj.org-article:af20fd880d2e426fadedd7d9a9f1c1342021-11-23T07:58:48ZMachine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review2640-456710.1002/aisy.202100073https://doaj.org/article/af20fd880d2e426fadedd7d9a9f1c1342021-11-01T00:00:00Zhttps://doi.org/10.1002/aisy.202100073https://doaj.org/toc/2640-4567Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling high‐throughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed.Shaobo LuoYuzhi ShiLip Ket ChinPaul Edward HutchinsonYi ZhangGiovanni ChierchiaHugues TalbotXudong JiangTarik BourouinaAi-Qun LiuWileyarticlecell detectiondeep learningimaging flow cytometryneural networksoptical sensingComputer engineering. Computer hardwareTK7885-7895Control engineering systems. Automatic machinery (General)TJ212-225ENAdvanced Intelligent Systems, Vol 3, Iss 11, Pp n/a-n/a (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
cell detection deep learning imaging flow cytometry neural networks optical sensing Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225 |
| spellingShingle |
cell detection deep learning imaging flow cytometry neural networks optical sensing Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225 Shaobo Luo Yuzhi Shi Lip Ket Chin Paul Edward Hutchinson Yi Zhang Giovanni Chierchia Hugues Talbot Xudong Jiang Tarik Bourouina Ai-Qun Liu Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review |
| description |
Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling high‐throughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed. |
| format |
article |
| author |
Shaobo Luo Yuzhi Shi Lip Ket Chin Paul Edward Hutchinson Yi Zhang Giovanni Chierchia Hugues Talbot Xudong Jiang Tarik Bourouina Ai-Qun Liu |
| author_facet |
Shaobo Luo Yuzhi Shi Lip Ket Chin Paul Edward Hutchinson Yi Zhang Giovanni Chierchia Hugues Talbot Xudong Jiang Tarik Bourouina Ai-Qun Liu |
| author_sort |
Shaobo Luo |
| title |
Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review |
| title_short |
Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review |
| title_full |
Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review |
| title_fullStr |
Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review |
| title_full_unstemmed |
Machine‐Learning‐Assisted Intelligent Imaging Flow Cytometry: A Review |
| title_sort |
machine‐learning‐assisted intelligent imaging flow cytometry: a review |
| publisher |
Wiley |
| publishDate |
2021 |
| url |
https://doaj.org/article/af20fd880d2e426fadedd7d9a9f1c134 |
| work_keys_str_mv |
AT shaoboluo machinelearningassistedintelligentimagingflowcytometryareview AT yuzhishi machinelearningassistedintelligentimagingflowcytometryareview AT lipketchin machinelearningassistedintelligentimagingflowcytometryareview AT pauledwardhutchinson machinelearningassistedintelligentimagingflowcytometryareview AT yizhang machinelearningassistedintelligentimagingflowcytometryareview AT giovannichierchia machinelearningassistedintelligentimagingflowcytometryareview AT huguestalbot machinelearningassistedintelligentimagingflowcytometryareview AT xudongjiang machinelearningassistedintelligentimagingflowcytometryareview AT tarikbourouina machinelearningassistedintelligentimagingflowcytometryareview AT aiqunliu machinelearningassistedintelligentimagingflowcytometryareview |
| _version_ |
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