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13 March 2024 Overview of learning-based models to enhance the imaging infrastructure
Ana Doblas, Carlos Trujillo
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030C (2024) https://doi.org/10.1117/12.3000095
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
The development of microscopic technologies has enhanced the translational research between scientists, engineers, biologists, and biomedical researchers, enabling the visualization and evaluation of complex biological systems. Advances in imaging systems are essential to further develop our understanding of cellular mechanisms and apply them to new diagnostic methods and disease treatment. Over the last decade, machine learning has been heavily used in microscopy image analysis, from the classification of cells to the reconstruction of real-time images, empowering the toolbox of automated microscopy. In this invited contribution, we discuss the use of generative adversarial networks in quantitative phase imaging and super-resolution microscopy.
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Ana Doblas and Carlos Trujillo "Overview of learning-based models to enhance the imaging infrastructure", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030C (13 March 2024); https://doi.org/10.1117/12.3000095
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KEYWORDS
Biological imaging
Machine learning
Systems modeling
3D image reconstruction
Gallium nitride
Imaging systems
Phase imaging
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