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.

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Aydogan Ozcan, "Deep learning-enabled computational microscopy and sensing (Conference Presentation)," Proc. SPIE 11087, Biosensing and Nanomedicine XII, 110870B (Presented at SPIE Nanoscience + Engineering: August 11, 2019; Published: 9 September 2019); https://doi.org/10.1117/12.2531534.6083788928001.