RSC Tokyo International Conference 2023
Deep Learning-enabled Computational Microscopy and Sensing Aydogan OZCAN UCLA, Los Angeles, USA
Biographical Sketch Dr. Aydogan Ozcan is the Chancellor’s Professor and the Volgenau Chair for Engineering Innovation at UCLA and an HHMI Professor with the Howard Hughes Medical Institute. He is also the Associate Director of the California NanoSystems Institute. Dr. Ozcan is elected Fellow of the National Academy of Inventors (NAI) and holds >60 issued/granted patents in microscopy, holography, computational imaging, sensing, mobile diagnostics, nonlinear optics and fiber-optics, and is also the author of one book and the co-author of >1000 peer-reviewed publications in leading scientific journals/conferences. Dr. Ozcan received major awards, including the Presidential Early Career Award for Scientists and Engineers (PECASE), International Commission for Optics ICO Prize, Dennis Gabor Award (SPIE), Joseph Fraunhofer Award & Robert M. Burley Prize (Optica), SPIE Biophotonics Technology Innovator Award, Rahmi Koc Science Medal, SPIE Early Career Achievement Award, Army Young Investigator Award, NSF CAREER Award, NIH Director’s New Innovator Award, Navy Young Investigator Award, IEEE Photonics Society Young Investigator Award and Distinguished Lecturer Award, National Geographic Emerging Explorer Award, National Academy of Engineering The Grainger Foundation Frontiers of Engineering Award and MIT’s TR35 Award for his seminal contributions to computational imaging, sensing and diagnostics. Dr. Ozcan is elected Fellow of Optica, AAAS, SPIE, IEEE, AIMBE, RSC, APS and the Guggenheim Foundation. Abstract We will discuss recently emerging applications of state-of-the-art deep learning methods on optical microscopy, microscopic image reconstruction and sensing, also covering their applications for mobile measurement systems (Fig. 1). Beyond its mainstream uses, such as recognizing and labeling specific image features, deep learning holds numerous opportunities for revolutionizing image formation, image reconstruction and sensing fields. In this presentation, I will provide an overview of some of our recent work [1-10] on the use of deep neural networks in advancing computational microscopy and sensing systems for various biomedical applications. References 1. Y. Rivenson, Z. Gorocs, H. Gunaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep Learning Microscopy,” Optica DOI: 10.1364/OPTICA.4.001437 (2017). 2. M. Kühnemund, Q. Wei, E. Darai, Y. Wang, I. Hernandez-Neuta, Z. Yang, D. Tseng, A, Ahlford, L. Mathot, T. Sjöblom, A. Ozcan, and M. Nilsson, “Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy,” Nature Communications DOI: 10.1038/NCOMMS13913 (2017) 3. H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydın, L.A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nature Methods DOI: 10.1038/s41592-018-0239-0 (2018) 4. Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L.A. Bentolila, C. Pritz and A. Ozcan, “Three- dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nature Methods DOI: 10.1038/s41592-019-0622-5 (2019)
RSC Tokyo International Conference, Makuhari Messe, Chiba, Japan, September 7-8, 2023.
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