http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Artificial intelligence in label-free microscopy
biological cell classification by time stretch
1 online resource (xxxiii, 134 pages) : color illustrations
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.
Preface; Acknowledgements; Contents; List of Figures; List of Tables; Part I Time Stretch Imaging; 1 Introduction; 2 Time Stretch; 2.1 Time Stretch Imaging; 2.2 Cell Classification Using Time Stretch Imaging; 2.3 Label-Free Phenotypic Screening; 2.4 Warped Time Stretch for Data Compression; Part II Inspection and Vision; 3 Nanometer-Resolved Imaging Vibrometer; 3.1 Introduction; 3.2 Experimental Demonstration; 3.3 Theoretical Study of the Vibrometer Performance; 3.4 Experimental Results; 3.5 Conclusion; 4 Three-Dimensional Ultrafast Laser Scanner; 4.1 Introduction.
4.2 Principle of Hybrid Dispersion Laser Scanner4.3 Applications of Hybrid Dispersion Laser Scanner; Part III Biomedical Applications; 5 Label-Free High-Throughput Phenotypic Screening; 5.1 Introduction; 5.2 Experimental Setup; 5.3 Results and Discussion; 5.4 Conclusion; 6 Time Stretch Quantitative Phase Imaging; 6.1 Background; 6.2 Time Stretch Quantitative Phase Imaging; 6.2.1 Overview; 6.2.2 Imaging System; 6.2.3 System Performance and Resolvable Points; 6.2.4 Microfluidic Channel Design and Fabrication; 6.2.5 Coherent Detection and Phase Extraction; 6.2.6 Cell Transmittance Extraction.
6.2.7 Image Reconstruction6.3 Image Processing Pipeline; 6.3.1 Feature Extraction; 6.3.2 Multivariate Features Enabled by Sensor Fusion; 6.3.3 System Calibration; 6.4 Conclusion; Part IV Big Data and Artificial Intelligence; 7 Big Data Acquisition and Processing in Real-Time; 7.1 Introduction; 7.2 Technical Description of the Acquisition System; 7.3 Big Data Acquisition Results; 7.4 Conclusion; 8 Deep Learning and Classification; 8.1 Background; 8.2 Machine Learning; 8.3 Applications; 8.3.1 Blood Screening: Demonstration in Classification of OT-II and SW-480 Cells.
8.3.2 Biofuel: Demonstration in Algae Lipid Content Classification8.4 Further Discussions in Machine Learning; 8.4.1 Learning Curves; 8.4.2 Principal Component Analysis (PCA); 8.4.3 Cross Validation; 8.4.4 Computation Time; 8.4.5 Data Cleaning; 8.5 Conclusion; Part V Data Compression; 9 Optical Data Compression in Time Stretch Imaging; 9.1 Background; 9.2 Warped Stretch Imaging; 9.3 Optical Image Compression; 9.4 Experimental Design and Results; 9.5 Conclusion; 10 Design of Warped Stretch Transform; 10.1 Overview; 10.2 Kernel Design; 10.2.1 Spectral Resolution.
10.2.2 Group Delay Profile Design10.2.3 Simulation Model; 10.2.4 Spectrograms; 10.3 Discussion; 10.4 Conclusion; 11 Concluding Remarks and Future Work; References; Index.
Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali.
Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
Includes bibliographical references and index.