Few-shot transfer learning for holographic image reconstruction using a recurrent neural network

Abstract

Deep learning-based methods in computational microscopy have been shown to be powerful but, in general, face some challenges due to limited generalization to new types of samples and requirements for large and diverse training data. Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets. We pre-trained a convolutional recurrent neural network on a dataset with three different types of samples and ∼2000 unique sample field-of-views, which serves as the backbone model. By fixing the trainable parameters of the recurrent blocks and transferring the rest of the convolutional blocks of the pre-trained model, we reduced the number of trainable parameters by ∼90% compared with standard transfer learning, while achieving equivalent generalization. We validated the effectiveness of this approach by successfully generalizing to new types of samples only using 80 unique field-of-views for training, and achieved (i) ∼2.5-fold convergence speed acceleration, (ii) ∼20% computation time reduction per epoch, and (iii) improved generalization to new sample types over baseline network models trained from scratch. This few-shot transfer learning approach can potentially be applied in other microscopic imaging methods, helping to generalize to new types of samples without the need for extensive training time and data.