Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning

TL;DR

This paper introduces a self-supervised contrastive projection learning method to automatically find semantic similarities in single-particle diffraction images, enabling large-scale, real-time analysis without human labeling.

Contribution

It extends contrastive learning with projection learning for diffraction images, achieving meaningful embeddings aligned with expert intuition, improving analysis efficiency.

Findings

Semantic embeddings align with physical intuition

Significant improvement over previous methods

Enables real-time large-scale analysis

Abstract

Single-shot diffraction imaging of isolated nanosized particles has seen remarkable success in recent years, yielding in-situ measurements with ultra-high spatial and temporal resolution. The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images, which are needed for structure determination of specimens with greater structural variety and for dynamic experiments. The size of the datasets, however, represents a monumental problem for their analysis. Here, we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling. By introducing the concept of projection learning, we extend self-supervised contrastive learning to the context of coherent diffraction imaging. As a result, we achieve a semantic dimensionality reduction producing meaningful embeddings that align with the physical intuition of an experienced human researcher. The method yields a substantial improvement compared to previous approaches, paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers.