Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction

Yair Rivenson and Aydogan Ozcan Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA Bioengineering Department, University of California, Los Angeles, CA, 90095, USA California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA http://innovate.ee.ucla.edu/welcome.html Abstract: We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally change both the hardware and image reconstruction methods used in optical microscopy in a holistic manner. Recent results in applications of deep learning [1] have proven to be transformative for various fields, redefining the state of the art results achieved by earlier machine learning techniques. As an example, one of the fields that has significantly benefited from the ongoing deep learning revolution is machine vision, with landmark results that enable new capabilities in autonomous cars, fault analysis, security applications, as well as entertainment.