Presentation
3 October 2024 Metasurface with deep learning for biomedical imaging applications
Author Affiliations +
Abstract
This talk explores the transformative power of deep learning (DL) in metasurface-based imaging systems. We will showcase how DL algorithms are pushing the boundaries of performance in different applications, including endo-microscopy, meta-miniscopy, and confocal microscopy. In endo-microscopy, we achieve high-resolution brain images with detailed vasculature within 0.1 seconds (50x faster) by combining DL networks with existing endoscopes. This significantly reduces image acquisition time and system complexity. For DL assisted meta-miniscope, the integration of DL models with a meta-miniscope facilitates the transformation of conventional bright-field images into edge-enhanced images with high contrast accuracy. This novel approach not only enhances image quality but also minimizes system complexity and processing times, thus opening new avenues for improved microscopy techniques. This talk will be of interest to researchers and developers in optics, bioimaging, and deep learning, to highlight significant advancements through metasurfaces with deep learning.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuan Luo "Metasurface with deep learning for biomedical imaging applications", Proc. SPIE PC13111, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XXII, PC131110Y (3 October 2024); https://doi.org/10.1117/12.3030915
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