Few-shot transfer learning using a recurrent neural network for hologram reconstruction (Conference Presentation)

Luzhe Huang; Xilin Yang; Tairan Liu; Aydogan Ozcan

doi:10.1117/12.2649194

16 March 2023 Few-shot transfer learning using a recurrent neural network for hologram reconstruction (Conference Presentation)

Luzhe Huang, Xilin Yang, Tairan Liu, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC123890B (2023) https://doi.org/10.1117/12.2649194
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

We report a novel few-shot transfer learning scheme based on a convolutional recurrent neural network architecture, which was used for holographic image reconstruction. Without sacrificing the hologram reconstruction accuracy and quality, this few-shot transfer learning scheme effectively reduced the number of trainable parameters during the transfer learning process by ~90% and improved the convergence speed by 2.5-fold over baseline models. This method can be applied to other deep learning-based computational microscopy and holographic imaging tasks, and facilitates the transfer learning of models to new types of samples with minimal training time and data.

Conference Presentation

Citation Download Citation

Luzhe Huang, Xilin Yang, Tairan Liu, and Aydogan Ozcan "Few-shot transfer learning using a recurrent neural network for hologram reconstruction (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC123890B (16 March 2023); https://doi.org/10.1117/12.2649194

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