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Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors (Case No. 2023-192)

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel platform technology to facilitate the design of all-optical visual processors, which can be used to perform advanced computational tasks at the speed of light. Background: Information processing via light is a cutting-edge field among optics...

Published: 4/5/2024 | Inventor(s): Aydogan Ozcan, MD Sadman Rahman, Xilin Yang

Keywords(s): Adaptive Optics, Algorithm Optical Coherence Tomography, all-optical diffractive computing, all-optical transformation, Artifical Intelligence (Machine Learning, Data Mining), Artificial Intelligence, Atomic Force Microscopy Optical Tweezers, computational imaging, deep diffractive network, Deep Learning, Deep learning-based sensing, diffractive processor, Dispersion (Optics), Electron Microscope, Electro-Optics, fluorescence microscopy, Focus (Optics), Infrared Electromagnetic Spectrum Dispersion (Optics), interference processor, large language model (LLNMs), linear optics, linear transformations, Machine Learning, Microscope, Microscopy, Microscopy And Imaging, Near-Field Scanning Optical Microscope, neural networks, Nonlinear Optics, non-linear optics, Optical Coherence , Optical Communication , Optical computing, Optical Fiber Copper Wire And Cable, optical implementation, Optical Microscope, Optical networks, optical processor, optical transmission, Optics Parabolic Reflector Curved Mirror, phase-only diffractive network, reverse engineered optical system, Software, Software & Algorithms, Software Development Tools, spatially-incoherent light, start to end optics design, Surgical Instrument Optical Coherence Tomography, three dimensional imaging, visual computing, Waferscale Processors

Category(s): Optics & Photonics, Optics & Photonics > Microscopy, Platforms, Software & Algorithms > Image Processing, Electrical, Electrical > Signal Processing, Electrical > Computing Hardware

Deep Learning-Based 3D Imaging of Fluorescent Samples from 2D Image (Case No. 2019-521)

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a deep learning-based approach termed Deep-Z that enables 3D imaging of fluorescent samples using a single 2D image, without mechanical scanning, additional hardware, or a trade-off resolution or speed. Background: Three-dimensional (3D) fluorescence...

Published: 2/16/2024 | Inventor(s): Aydogan Ozcan, Yair Rivenson, Yichen Wu

Keywords(s): Image Processing, Imaging, Medical Imaging, Microscope, Microscopy And Imaging

Category(s): Electrical, Electrical > Imaging, Life Science Research Tools > Microscopy And Imaging

2014-9AG Wide-Field Computational Imaging of Pathology Slides Using Lensfree On-Chip Microscopy

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a wide field-of-view (FOV) computational imaging method using lens-free on-chip microscopy to efficiently and cost-effectively scan biological samples such as pathology slides. Background: Wide field-of-view (FOV) and high-resolution imaging is crucial...

Published: 7/19/2023 | Inventor(s): Aydogan Ozcan, Alon Greenbaum, Yibo Zhang, Alborz Feizi, Wei Luo

Category(s): Life Science Research Tools, Electrical, Electrical > Imaging, Life Science Research Tools > Microscopy And Imaging, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Diagnostic Markers, Diagnostic Markers > Targets And Assays, Medical Devices > Medical Imaging

2019-737 Deep Learning-Based Color Holographic Microscopy

Summary: UCLA researchers in the Department of Electrical Engineering have developed a novel deep learning-based method that performs high-fidelity color image reconstruction using a single hologram. Background: Pathology slides are currently the gold standard in diagnostics for many diseases. Accurate color representations of well stained pathology...

Published: 7/19/2023 | Inventor(s): Aydogan Ozcan, Yair Rivenson, Tairan Liu, Yibo Zhang, Zhensong Wei

Keywords(s): Artifical Intelligence (Machine Learning, Data Mining), Artificial Neural Network, Computer Aided Learning, Confocal Microscopy, Cytopathology, Digital Holography, Electron Microscope, Fluorescence Microscope, Fluorescence-Lifetime Imaging Microscopy Leading Lights, Histology, Histopathology, Holography, Image Resolution, Imaging, Immunohistochemistry, Machine Learning, Magnetic Resonance Imaging Pathology, Medical Imaging, Microscope, Microscopy, Microscopy And Imaging, Neuropathology, Pathogen, Pathogenesis, Pathophysiology, Perceptual Learning, Unsupervised Learning , Wavelength

Category(s): Electrical, Electrical > Imaging, Life Science Research Tools, Life Science Research Tools > Microscopy And Imaging, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Diagnostic Markers, Medical Devices > Medical Imaging