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9 March 2020 Deep learning enables color holographic microscopy of pathology slides from a single hologram (Conference Presentation)
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Abstract
We report a deep learning-based colorization framework for holographic microscopy, and demonstrate its efficacy by imaging histopathology slides (Masson’s trichrome-stained lung and H&E-stained prostate tissue). Using a generative adversarial network, this framework is trained to eliminate the missing-phase-related artifacts. To obtain accurate color information, the pathology slides were imaged under multiplexed illumination at three wavelengths, and the deep network learns to demultiplex and project the holographic images from the three color channels into the RGB color-space, achieving high color-fidelity. Our method dramatically simplifies the data acquisition and shortens the processing time, which is important for e.g., digital pathology in resource-limited-settings.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tairan Liu, Zhensong Wei, Yair Rivenson, Kevin de Haan, Yibo Zhang, Yichen Wu, and Aydogan Ozcan "Deep learning enables color holographic microscopy of pathology slides from a single hologram (Conference Presentation)", Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300T (9 March 2020); https://doi.org/10.1117/12.2546956
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