论文标题
FMRI自闭症谱系障碍的深入强化学习
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
论文作者
论文摘要
目的：由于功能性MRI（fMRI）数据集通常很小，因此我们寻求一种静止状态fMRI自闭症谱系障碍（ASD）与神经型（NT）控制的方法有效的方法。我们假设深入的增强学习（DRL）分类器可以在小型的fMRI培训集中有效学习。 方法：我们从自闭症脑成像数据交换（Abide）数据库的100个图形标签对上训练了深钢筋学习（DRL）分类器。为了进行比较，我们在同一培训集中培训了一个有监督的深度学习（SDL）分类器。 结果：DRL明显优于SDL，P值为2.4 x 10^（-7）。 DRL为各种分类器性能指标取得了卓越的结果，包括F1分数为76，而SDL为67。 SDL迅速过度介绍了培训数据，而DRL以渐进的方式学习了对单独的测试集的推广方式。 结论：DRL可以学习以数据有效的方式对ASD与NT进行分类，这是为小型培训集进行的。未来的工作将涉及优化神经网络以提高数据效率并将方法应用于其他功能磁共振成像数据集，即脑癌患者。
Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) versus neurotypical (NT) controls. We hypothesized that a Deep Reinforcement Learning (DRL) classifier could learn effectively on a small fMRI training set. Methods : We trained a Deep Reinforcement Learning (DRL) classifier on 100 graph-label pairs from the Autism Brain Imaging Data Exchange (ABIDE) database. For comparison, we trained a Supervised Deep Learning (SDL) classifier on the same training set. Results : DRL significantly outperformed SDL, with a p-value of 2.4 x 10^(-7). DRL achieved superior results for a variety of classifier performance metrics, including an F1 score of 76, versus 67 for SDL. Whereas SDL quickly overfit the training data, DRL learned in a progressive manner that generalised to the separate testing set. Conclusion : DRL can learn to classify ASD versus NT in a data efficient manner, doing so for a small training set. Future work will involve optimizing the neural network for data efficiency and applying the approach to other fMRI data sets, namely for brain cancer patients.
