串行飞秒晶体学中模式分类的弱监督学习
Weakly supervised learning for pattern classification in serial femtosecond crystallography
AI总结：
本文针对串行飞秒晶体学中衍射图样标注成本高的问题，提出用弱监督算法进行分类，证明其可在显著减少标注数据量的同时达到与全监督方法相当的准确率。
摘要：
X射线自由电子激光装置上的串行飞秒晶体学为晶体结构测定开辟了新时代。然而，这些实验的数据处理正面临前所未有的挑战，因为测定高分辨率结构所需的衍射图样总数非常庞大。机器学习方法很可能在处理如此大规模数据中发挥重要作用。卷积神经网络在模式分类领域已取得巨大成功，但网络训练需要带有标签的大规模数据集。这种对标注数据集的严重依赖将严重限制网络的应用，因为标注大量衍射图样的成本非常高。本文介绍了我们利用弱监督算法进行衍射图样分类的工作，目的是尽可能减少训练所需标注数据集的规模。我们的结果表明，弱监督方法可以显著减少所需标注图样的数量，同时达到与全监督方法相当的准确率。
英文摘要：
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.
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