| Autor: |
Xin-Hua Hu, Jun Q. Lu, Peng Tian, Yuhua Wen, Jiahong Jin |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Advances in Microscopic Imaging II. |
| DOI: |
10.1117/12.2526892 |
| Popis: |
Rapid and label-free cell assay presents a challenging and significant problem that have wide applications in life science and clinics. We report here a method that combines polarization diffraction imaging flow cytometry (p-DIFC) with deep convolutional neural network (CNN) based image analysis for solving the above problem. Cross-polarized diffraction image (p-DI) pairs were acquired from 6185 cells in 5 types to investigate their uses for accurate classification. Different CNN architects have been studied to develop a compact architect named DINet which has relatively small set of network parameter for fast training and test. The averaged accuracy among the 5 groups of p-DI data ranges from 98.7% to 99.2%. With the DINet, the strong potentials of the p-DIFC method for morphology based and label-free cell assay have been demonstrated. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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