Artificial Intelligence in Label-free Microscopy
Biological Cell Classification by Time Stretch
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Beschreibung
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.
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. Includes supplementary material: sn.pub/extras
Autor*in
Ata Mahjoubfar
Themen in »Artificial Intelligence in Label-free Microscopy«
silicon photonics real-time instruments for biomedical applications High-throughput multivariate sensing ultrafast and high-throughput data acquisition warped time stretch