基于多影像中心磁共振成像数据的半监督膝盖异常分类  

Semi-supervised knee abnormality classification based on multi-imaging center MRI data

作　　者：吴洁[1,2] 张师天 谢海滨 杨光[1,2] WU Jie;ZHANG Shitian;XIE Haibin;YANG Guang(School of Physics and Electronic Science,East China Normal University,Shanghai 200241,China;Shanghai Key Laboratory of Magnetic Resonance(East China Normal University),Shanghai 200062,China;School of Physics and Astronomy,University of Edinburgh,Edinburgh EH89YL,UK)

机构地区：[1]华东师范大学物理与电子科学学院,上海200241 [2]上海市磁共振重点实验室(华东师范大学),上海200062 [3]爱丁堡大学物理与天文学院,英国爱丁堡EH89YL

出　　处：《计算机应用》2022年第1期316-324,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61731009)。

摘　　要：针对大量数据手工标记的繁重性和单一影像中心磁共振成像(MRI)数据的有限性问题,提出了一种利用多影像中心有标签与无标签MRI数据的用于磁共振的半监督学习(MRSSL)方法,并将其应用在膝盖异常分类任务中。首先,运用了数据扩增方法来提供模型所需的归纳偏置;接着,融合了分类损失项和一致性损失项来约束人工神经网络并使之从数据中提取出具有辨别力的特征;然后,将这些特征用于MRI膝盖异常分类。此外,也提出了对应的仅利用有标签数据的完全监督学习(MRSL)方法。在给出同样的有标签样本时,将MRSL与MRSSL进行了比较,结果表明MRSSL的模型分类性能与泛化性能明显优于MRSL。最后,将MRSSL与其他半监督学习方法进行了比较。结果表明数据扩增在性能提升中起到了重要作用,并且MRSSL凭借更强的MRI数据包容性取得了最优的膝盖异常分类性能。The manual labeling of abundant data is laborious and the amount of Magnetic Resonance Imaging(MRI)data from a single imaging center is limited.Concerning the above problems,a Magnetic Resonance Semi-Supervised Learning(MRSSL)method utilizing multi-imaging center labeled and unlabeled MRI data was proposed and applied to knee abnormality classification.Firstly,data augmentation was used to provide the inductive bias required by the model.Next,the classification loss and the consistency loss were combined to constraint an artificial neural network to extract the discriminative features from the data.Then,the features were used for the MRI knee abnormality classification.Additionally,the corresponding Magnetic Resonance Supervised Learning(MRSL)method only using labeled samples was proposed and compared with MRSSL for the same labeled samples.The results demonstrate that MRSSL surpasses MRSL in both model classification performance and model generalization ability.Finally,MRSSL was compared with other semi-supervised learning methods.The results indicate that data augmentation plays an important role on performance improvement,and with stronger inclusiveness for MRI data,MRSSL outperforms others on the knee abnormality classification.

关 键 词：深度学习 半监督学习 磁共振成像 多影像中心数据 膝盖异常分类 MRNet数据集 OAI数据集 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]