Prof. Aydogan Ozcan: Deep Learning-enabled Computational Microscopy and Diffractive Imaging

Recording of prof. Aydogan Ozcan's talk on March 20, 2024, at the EPFL Seminar Series in Imaging.
Abstract: In this presentation, I will provide an overview of our recent work on using deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications, including virtual staining of label-free tissue for pathology. I will also discuss diffractive optical networks designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially-engineered surfaces. These diffractive processors designed by deep learning have various applications, e.g., all-optical image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components such as spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) all-optical statistical inference engines, (2) computational camera and microscope designs and (3) inverse design of optical systems that are task-specific. In this talk, I will give examples of each group, enabling transformative capabilities for various applications of interest in e.g., autonomous systems, defense/security, telecommunications as well as biomedical imaging and sensing.