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Proceedings Paper

Deep learning-enabled computational microscopy and sensing (Conference Presentation)
Author(s): Aydogan Ozcan

Paper Abstract

Deep learning is a class of machine learning techniques that uses multi-layered artificial neural networks for automated analysis of signals or data. The name comes from the general structure of deep neural networks, which consist of several layers of artificial neurons, each performing a nonlinear operation, stacked over each other. Beyond its main stream applications such as the recognition and labeling of specific features in images, deep learning holds numerous opportunities for revolutionizing image formation, reconstruction and sensing fields. In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11258, Frontiers in Biological Detection: From Nanosensors to Systems XII, 112580K (10 March 2020);
Show Author Affiliations
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11258:
Frontiers in Biological Detection: From Nanosensors to Systems XII
Amos Danielli; Benjamin L. Miller; Sharon M. Weiss, Editor(s)

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