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20 August 2020 Deep learning-based holographic reconstruction for color imaging of pathology slides
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Abstract
We present a deep learning-based, high-throughput, accurate colorization framework for holographic imaging systems. Using a conditional generative adversarial network (GAN), this method can be used to remove the missing-phase-related spatial artifacts using a single hologram. When compared to the absorbance spectrum estimation method, which is the current state-of-the art method used to perform color holographic reconstruction, this framework is able to achieve a similar performance while requiring 4-fold fewer input images and 8-fold less imaging and processing time. The presented method can effectively increase the throughput for color holographic microscopy, providing the possibility for histopathology in resource limited environment.
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Tairan Liu, Zhensong Wei, Yair Rivenson, Kevin de Haan, Yibo Zhang, Yichen Wu, and Aydogan Ozcan "Deep learning-based holographic reconstruction for color imaging of pathology slides", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690E (20 August 2020); https://doi.org/10.1117/12.2567515
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