论文标题
关于自学学习对大脑解码的好处
On the benefits of self-taught learning for brain decoding
论文作者
论文摘要
语境。我们研究了在自学成才的学习框架中使用由fMRI统计图组成的大型公共神经影像学数据库的好处，以改善大脑解码的新任务。首先，我们利用Neurovault数据库在选择相关统计图（卷积自动编码器的选择）上训练，以重建这些地图。然后，我们使用此训练有素的编码器来初始化有监督的卷积神经网络，以从神经循环数据库的大量集合中对任务或认知过程进行分类。结果。我们表明，这种自学成才的学习过程总是可以提高分类器的性能，但是好处的幅度在很大程度上取决于用于预训练和填充模型的样本数量以及目标下游任务的复杂性。结论。预先训练的模型改善了分类性能，并显示出更具概括性的特征，对个体差异的敏感性降低。
Context. We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. Results. We show that such a self-taught learning process always improves the performance of the classifiers but the magnitude of the benefits strongly depends on the number of samples available both for pre-training and finetuning the models and on the complexity of the targeted downstream task. Conclusion. The pre-trained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.
