  In tomographic reconstruction, the image quality of the reconstructed images
can be significantly degraded by defects in the measured two-dimensional (2D)
raw image data. Despite the importance of screening defective 2D images for
robust tomographic reconstruction, manual inspection and rule-based automation
suffer from low-throughput and insufficient accuracy, respectively. Here, we
present deep learning-enabled quality control for holographic data to produce
robust and high-throughput optical diffraction tomography (ODT). The key idea
is to distill the knowledge of an expert into a deep convolutional neural
network. We built an extensive database of optical field images with
clean/noisy annotations, and then trained a binary classification network based
upon the data. The trained network outperformed visual inspection by non-expert
users and a widely used rule-based algorithm, with > 90% test accuracy.
Subsequently, we confirmed that the superior screening performance
significantly improved the tomogram quality. To further confirm the trained
model's performance and generalizability, we evaluated it on unseen biological
cell data obtained with a setup that was not used to generate the training
dataset. Lastly, we interpreted the trained model using various visualization
techniques that provided the saliency map underlying each model inference.
