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13 March 2019 Super-resolution microscopy using deep learning (Conference Presentation)
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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 for achieving super-resolution in optical microscopy across different imaging modalities.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aydogan Ozcan "Super-resolution microscopy using deep learning (Conference Presentation)", Proc. SPIE 10884, Single Molecule Spectroscopy and Superresolution Imaging XII, 108840S (13 March 2019); https://doi.org/10.1117/12.2509120
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