基于噪声混淆增强特征鲁棒性的脑疾病预测

Brain Disease Prediction Based on Noise Confusion to Enhance Robustness of Fea⁃tures

随着医学影像数据的不断发展,纵向数据分析逐渐成为了解和跟踪阿尔茨海默病(Alzheimer’s disease,AD)发病过程的重要研究方向。目前已经提出了许多纵向数据分析方法,其中多任务学习得到广泛应用,它能够集成多个时间点的影像数据,提高模型的泛化能力。大多数现有的方法能够识别不同时间点的共享特征,但这些特征中会包含一定的噪声。与此同时,不同时间点进展的潜在关联仍未得到充分的探索。本文提出了一种基于参数分解和关系诱导的多任务学习(Parameter decomposition and relation⁃induced multi⁃task learning,PDRIMTL)方法,以此从纵向数据中识别特征。该方法不仅能够识别去除噪声后的共享特征,提高共享特征的鲁棒性,而且能够对不同时间点的内在关联进行建模。结果表明,在不同时间点的结构磁共振成像(Structural magnetic resonance imaging,sMRI)数据上,该模型能够有效提高对AD鉴别的准确性。

With the continuous development of medical imaging data,longitudinal data analysis is gradually becoming an important research direction to understand and trace the process of the Alzheimer’s disease(AD).At present,many longitudinal data analysis methods have been proposed,among which multi-task learning is widely used,which can integrate imaging data of multiple time points to improve the generalization ability of the model.Most existing methods can identify shared features at different time points,but these features will contain a certain amount of noise.At the same time,potential associations of disease progression at different time points remain under explored.In this paper,we propose a parameter decomposition and relation-induced multi-task learning(PDRIMTL)method to identify features from longitudinal data.The method can not only identify shared features after noise removal and improve the robustness of shared features,but also can model the intrinsic associations between different time points.The results show that the model can effectively improve the accuracy of AD identification on structural magnetic resonance imaging(sMRI)data at different time points.