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

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.