基于双模态的小角散射图像结构表征技术研究

Research on bimodal SAXS image structure characterization technique

小角X射线散射设备的不断升级和发展产生了更多更高维的散射数据,给研究人员快速获取实验结果带来了极大挑战。亟需有效的自动化分类方法,加快数据表征速度的同时保证较高的准确率。然而,许多模型学习特征主要针对光照图像,忽略了散射图像特点,分类准确率较低。因此,基于散射模式特点,提出了一种双模态细粒度特征提取模型BRTNet。该模型采用双模态输入模式,其一为采用多尺度卷积为架构的特征学习网络PRS,学习散射图像的微观信息;其二为融合局部信息的多头注意力机制ConvTransformer,学习散射序列的相关性信息。然后,模型结合图像信息和序列信息,融合双分支特征,对散射数据进行分类并获得分类结果。在生物溶液散射数据集上的实验结果表明,模型分类准确率超89%,同基准模型相比具有较为明显的优势。

The continuous upgrading and development of small-angle X-ray scattering(SAXS)equipment have generated more high-dimensional scattering data,which poses great challenges for researchers to quickly obtain experimental results.Researchers urgently need effective automated classification methods to speed up data representation and obtain higher accuracy.However,many models learn features mainly for illumination images,ignoring the characteristics of scattering images and resulting in lower classification accuracy.Therefore,based on the characteristics of scattering patterns,this paper proposes a bimodal fine-grained feature extraction model called BRTNet.The model adopts a bimodal input mode.The first mode is the feature learning network PRS using a multi-scale convolution architecture,which learns the micro-information of scattering images.The second mode is the multi-head attention mechanism ConvTransformer fusing local information,which learns the correlation information of scattering sequences.Then,the model combines image information and sequence information,fuses the dual-branch features,classifies the scattering data,and obtains the classification results.Experimental results on the biological solution scattering dataset show that the model's classification accuracy exceeds 89%,which has a significant advantage over the baseline model.