Artificial Intelligence in Label-free Microscopy

I Time stretch imaging1 Introduction2 Background2.1 Time Stretch Imaging2.2 Cell Classification Using Time Stretch Imaging2.3 Label-free Phenotypic Screening2.4 Warped Time Stretch for Data CompressionII Inspection and vision3 Nanometer-resolved imaging vibrometer3.1 Introduction3.2 Experimental demonstration3.3 Theoretical study of the vibrometer performance3.4 Experimental results3.5 Conclusion4 Three-dimensional ultrafast laser scanner4.1 Introduction4.2 Principle of hybrid dispersion laser scanner4.3 Applications of hybrid dispersion laser scannerIII Biomedical applications5 Label-free High-throughput Phenotypic Screening5.1 Introduction5.2 Experimental Setup5.3 Results and Discussion5.4 Conclusion6 Time Stretch Quantitative Phase Imaging6.1 Background6.2 Time Stretch Quantitative Phase Imaging6.2.1 Overview6.2.2 Imaging system6.2.3 System performance and resolvable points6.2.4 Microuidic channel design and fabrication6.2.5 Coherent Detection and Phase Extraction6.2.6 Cell Transmittance Extraction6.2.7 Image Reconstruction6.3 Image Processing Pipeline6.3.1 Feature Extraction6.3.2 Multivariate Features Enabled by Sensor Fusion6.3.3 System Calibration6.4 ConclusionIV Big data and artifitial intelligence7 Big data acquisition and processing in real-time7.1 Introduction7.2 Technical description of the acquisition system7.3 Big data acquisition results7.4 Conclusion8 Deep Learning and Classification8.1 Background8.2 Machine Learning8.3 Applications8.3.1 Blood Screening: Demonstration in Classification of OT-II and SW-480 Cells8.3.2 Biofuel: Demonstration in Algae Lipid Content Classification8.4 Further Discussions in Machine Learning8.4.1 Learning Curves8.4.2 Principal Component Analysis (PCA)8.4.3 Cross Validation8.4.4 Computation Time8.4.5 Data Cleaning8.5 ConclusionV Data compression9 Optical Data Compression in Time Stretch Imaging9.1 Background9.2 Warped Stretch Imaging9.3 Optical Image Compression9.4 Experimental Design and Results9.5 Conclusion10 Design of

Ata Mahjoubfar is a postdoctoral scholar in the Department of Electrical Engineering and California NanoSystems Institute at University of California Los Angeles. He received his Bachelor's and Master's degrees from University of Tehran, Iran in 2006 and 2008, respectively, and his Ph.D. degree from University of California, Los Angeles (UCLA) in 2014. He was the cofounder of OSA/SPIE student chapter at UCLA and its president in 2012. He is the author of more than 35 peer-reviewed publications, and he holds two international patents. His research interests include artificial intelligence, machine vision and learning, imaging and visualization, ultrafast data acquisition and analytics, biomedical technology, and financial engineering.
Claire Lifan Chen is a senior application engineer at Lumentum Operations LLC. She received her Ph.D. degree in Electrical Engineering and M.Sc. degree in Bioengineering at University of California, Los Angeles in 2015 and 2012, respectively. She received her B.Sc. degree in Optics Science and Engineering from Fudan University, China in 2010. Dr. Chen was a member of California NanoSystems Institute from 2013 to 2015 and the president of OSA/SPIE student chapter at UCLA in 2015. She has authored and co-authored 14 peer-reviewed publications and 2 patents. Her research interests include machine learning, data acquisition and analytics, image processing, and high-throughput imaging with applications in biomedical and information technologies.
Bahram Jalali is the Northrop-Grumman Endowed Chair and Professor of Electrical Engineering at UCLA with joint appointments in Biomedical Engineering, California NanoSystems Institute (CNSI) and Department of Surgery at the UCLA School of Medicine. He is the inventor of the Photonic Time Stretch, a measurement technique that has led to discoveries of new scientific phenomena and to technological inventions. He received his Ph.D. in Applied Physics from Columbia University in 1989 and was with Bell