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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| Autores principales: | , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/af20fd880d2e426fadedd7d9a9f1c134 |
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| Sumario: | 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. |
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