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Artificial Intelligence in Label-free Microscopy

Biological Cell Classification by Time Stretch

  • Ata Mahjoubfar
  • Claire Lifan Chen
  • Bahram Jalali

Table of contents

  1. Front Matter
    Pages i-xxxiii
  2. Time Stretch Imaging

    1. Front Matter
      Pages 1-1
    2. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 3-5
    3. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 7-11
  3. Inspection and Vision

    1. Front Matter
      Pages 13-13
    2. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 15-20
    3. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 21-29
  4. Biomedical Applications

    1. Front Matter
      Pages 31-31
    2. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 33-41
    3. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 43-63
  5. Big Data and Artificial Intelligence

    1. Front Matter
      Pages 65-65
    2. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 67-71
    3. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 73-85
  6. Data Compression

    1. Front Matter
      Pages 87-87
    2. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 89-99
    3. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 101-119
    4. Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
      Pages 121-122
  7. Back Matter
    Pages 123-134

About this book

Introduction

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.

Keywords

silicon photonics real-time instruments for biomedical applications High-throughput multivariate sensing ultrafast and high-throughput data acquisition warped time stretch

Authors and affiliations

  • Ata Mahjoubfar
    • 1
  • Claire Lifan Chen
    • 2
  • Bahram Jalali
    • 3
  1. 1.University of California Los AngelesLos AngelesUSA
  2. 2.University of California Los AngelesLos AngelesUSA
  3. 3.University of California Los AngelesLos AngelesUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-51448-2
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-51447-5
  • Online ISBN 978-3-319-51448-2
  • Buy this book on publisher's site
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