基于自适应多任务卷积神经网络的脑网络分类方法

Brain Networks Classification Based on an Adaptive Multi-Task Convolutional Neural Networks

脑网络分类是脑科学研究中的一项重要课题.近年来,基于卷积神经网络的脑网络分类方法已经成为一个前沿热点.然而,目前仍难以对数据维度高、样本量小的脑网络数据进行精准分类.由于不同人群的临床表型与其脑网络差异存在着一定的依存关系,极有可能为脑网络分类提供辅助信息,故提出一种新的基于自适应多任务卷积神经网络的脑网络分类方法.该方法引入临床表型预测作为辅助任务,通过多任务卷积神经网络的共享表示机制来为脑网络分类提供有用信息;同时为了降低实验成本和人工操作带来的误差,提出了一种新的自适应方法来代替人工调整多任务学习中各个子任务的权重.在ABIDE I(autism brain imaging data exchange I)数据集上的实验结果表明:引入临床表型预测任务的多任务卷积神经网络能够获得更好的脑网络分类结果,而且自适应多任务学习方法能够进一步提升脑网络的分类性能.

Brain networks classification is an important subject in brain science.In recent years,brain networks classification based on convolutional neural networks has become a hot topic.However,it is still difficult to accurately classify brain network data with high dimension and small sample size.Due to the close relationship between different clinical phenotypes and brain networks of different populations,it is highly possible to provide auxiliary information for the brain networks classification.Therefore,we propose a new brain networks classification method based on an adaptive multi-task convolutional neural network in this paper.Firstly,the clinical phenotype predictions are introduced as different auxiliary tasks and the shared representation mechanism of multi-task convolutional neural networks is used to provide general and useful information for brain networks classification.Then,in order to reduce the experimental cost and the error caused by the manual operation,a new adaptive method is proposed to substitute for manual adjustments of the weight of every task in the multi-task learning.The experimental results on the autism brain imaging data exchange I(ABIDE I)dataset show that the multi-task convolutional neural networks which introduce clinical phenotype predictions can achieve better classification results.Moreover,the adaptive multi-task learning method can further improve the performance of brain networks classification.