SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

TL;DR

SpeckleNN is a neural network-based embedding model designed for real-time classification of speckle patterns in X-ray imaging, effective with limited labeled data and capable of handling high data rates in SPI experiments.

Contribution

The paper introduces SpeckleNN, a scalable, few-shot embedding approach for speckle pattern classification in X-ray imaging, enabling real-time analysis with minimal labels.

Findings

Effective few-shot classification on new samples

Robust performance with limited labels and missing data

Scales linearly with dataset size

Abstract

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or "speckles", to extract single hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high data rate facilities like European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.