Weakly supervised learning for pattern classification in serial femtosecond crystallography

TL;DR

This paper introduces a weakly supervised learning approach for classifying diffraction patterns in serial femtosecond crystallography, reducing the need for extensive labeled data while maintaining high accuracy.

Contribution

It presents a novel weakly supervised algorithm that minimizes labeled data requirements for diffraction pattern classification in crystallography.

Findings

Weakly supervised methods reduce labeling effort significantly.

Achieves comparable accuracy to fully supervised models.

Demonstrates effectiveness on crystallography diffraction data.

Abstract

Serial femtosecond crystallography at X-ray free electron laser facilities opens a new era for the determination of crystal structure. However, the data processing of those experiments is facing unprecedented challenge, because the total number of diffraction patterns needed to determinate a high-resolution structure is huge. Machine learning methods are very likely to play important roles in dealing with such a large volume of data. Convolutional neural networks have made a great success in the field of pattern classification, however, training of the networks need very large datasets with labels. Th is heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns. In this article we present our job on the classification of diffraction pattern by weakly supervised algorithms, with the aim of reducing as much as possible the size of the labeled dataset required for training. Our result shows that weakly supervised methods can significantly reduce the need for the number of labeled patterns while achieving comparable accuracy to fully supervised methods.