Few-shot generalizable hologram reconstruction model using a recurrent neural network (RNN) (Conference Presentation)

Luzhe Huang; Xilin Yang; Tairan Liu; Aydogan Ozcan

doi:10.1117/12.2632636

4 October 2022 Few-shot generalizable hologram reconstruction model using a recurrent neural network (RNN) (Conference Presentation)

Luzhe Huang, Xilin Yang, Tairan Liu, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040H (2022) https://doi.org/10.1117/12.2632636
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Deep learning-based microscopic imaging methods commonly have limited generalization to new types of samples, requiring diverse training image data. Here we report a few-shot transfer learning framework for hologram reconstruction that can rapidly generalize to new types of samples using only small amounts of training data. The effectiveness of this method was validated on small image datasets of prostate and salivary gland tissue sections unseen by the network before. Compared to baseline models trained from scratch, our approach achieved ~2.5-fold convergence speed acceleration, ~20% training time reduction per epoch, and improved image reconstruction quality.

Conference Presentation

Citation Download Citation

Luzhe Huang, Xilin Yang, Tairan Liu, and Aydogan Ozcan "Few-shot generalizable hologram reconstruction model using a recurrent neural network (RNN) (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040H (4 October 2022); https://doi.org/10.1117/12.2632636

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