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Artificial Intelligence in Label-free Microscopy [electronic resource] : Biological Cell Classification by Time Stretch / by Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali.

By: Mahjoubfar, Ata [author.].
Contributor(s): Chen, Claire Lifan [author.] | Jalali, Bahram [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XXXIII, 134 p. 52 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319514482.Subject(s): Biomedical engineering | Electronics | Computer vision | Bioinformatics | Biomedical Engineering and Bioengineering | Electronics and Microelectronics, Instrumentation | Computer Vision | BioinformaticsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 610.28 Online resources: Click here to access online
Contents:
Introduction -- Background -- Nanometer-resolved imaging vibrometer -- Three-dimensional ultrafast laser scanner -- Label-free High-throughput Phenotypic Screening -- Time Stretch Quantitative Phase Imaging -- Big data acquisition and processing in real-time -- Deep Learning and Classification -- Optical Data Compression in Time Stretch Imaging -- Design of Warped Stretch Transform -- Concluding Remarks and Future Work -- References.
In: Springer Nature eBookSummary: This book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis. • Demonstrates how machine learning is used in high-speed microscopy imaging to facilitate medical diagnosis; • Provides a systematic and comprehensive illustration of time stretch technology; • Enables multidisciplinary application, including industrial, biomedical, and artificial intelligence.
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Introduction -- Background -- Nanometer-resolved imaging vibrometer -- Three-dimensional ultrafast laser scanner -- Label-free High-throughput Phenotypic Screening -- Time Stretch Quantitative Phase Imaging -- Big data acquisition and processing in real-time -- Deep Learning and Classification -- Optical Data Compression in Time Stretch Imaging -- Design of Warped Stretch Transform -- Concluding Remarks and Future Work -- References.

This book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis. • Demonstrates how machine learning is used in high-speed microscopy imaging to facilitate medical diagnosis; • Provides a systematic and comprehensive illustration of time stretch technology; • Enables multidisciplinary application, including industrial, biomedical, and artificial intelligence.

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