  Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is
particularly well suited to determine the 3D structure of particles in their
native environment. For a successful reconstruction, diffraction patterns
originating from a single hit must be isolated from a large number of acquired
patterns. We propose to formulate this task as an image classification problem
and solve it using convolutional neural network (CNN) architectures. Two CNN
configurations are developed: one that maximises the F1-score and one that
emphasises high recall. We also combine the CNNs with expectation maximization
(EM) selection as well as size filtering. We observed that our CNN selections
have lower contrast in power spectral density functions relative to the EM
selection, used in our previous work. However, the reconstruction of our
CNN-based selections gives similar results. Introducing CNNs into SPI
experiments allows streamlining the reconstruction pipeline, enables
researchers to classify patterns on the fly, and, as a consequence, enables
them to tightly control the duration of their experiments. We think that
bringing non-standard artificial intelligence (AI) based solutions in a
well-described SPI analysis workflow may be beneficial for the future
development of the SPI experiments.
