Weakly supervised learning for pattern classification in serial femtosecond crystallography. (arXiv:2309.04474v1 [cond-mat.mtrl-sci])

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.