一种基于张量分解的医学数据缺失模态的补全算法

A Complete Algorithm for Missing Modalities of Medical Data Based on Tensor Decomposition

多模态磁共振影像数据采集过程中会出现不同程度的模态数据缺失,现有的补全方法大多只针对随机缺失,无法较好地恢复条状及块状缺失。针对此问题,本文提出了一种基于多向延迟嵌入的平滑张量补全算法分类框架。首先,对缺失数据进行多向延迟嵌入操作,得到折叠后的张量;然后通过平滑张量CP分解,得到补全的张量;最后利用多向延迟嵌入的逆向操作,得到补全的数据。该算法在BraTS脑胶质瘤影像数据集上进行了高低级别肿瘤分类实验,并与7种基线模型进行了比较。实验结果表明,本文提出方法的平均分类准确率可达91.31%,与传统补齐算法相比具有较好的准确性。

In the process of multi-modality magnetic resonance image(MRI)data acquisition,there will be different degrees of modality data missing.However,most of the existing completion methods only aim at random missing,which cannot recover strip and block missing.Therefore,this paper proposes a classification framework of smooth tensor completion algorithm based on multi-directional delay embedding.Firstly,the folded tensor is obtained by multi-directional delay embedding of missing data.Then,the completed tensor is obtained by smoothing tensor CP decomposition.Finally,the reverse operation of multi-directional delay embedding is used to obtain the completed data.The algorithm is used to classify high-level and low-level tumors on the BraTS glioma image data set and compared with seven baseline models.The average classification accuracy of the proposed method achieves 91.31%,and experimental results show that the method has better accuracy compared with the traditional complement algorithm.