论文标题
FADER网络用于fMRI：ABIDE-II研究
Fader Networks for domain adaptation on fMRI: ABIDE-II study
论文作者
论文摘要
遵守是具有fMRI数据和完整表型描述的最大开源自闭症谱系障碍数据库。根据功能连接分析以及对原始数据的深入学习，对这些数据进行了广泛的研究，对于单独的扫描站点，顶级模型的精度接近75 \％。然而，仍然存在模型在遵守内部不同扫描位点之间可转移的问题。在当前论文中，我们首次在原始神经成像数据上对脑病理学分类问题进行域适应性。我们使用3D卷积自动编码器来构建与潜在空间图像表示无关的域，并演示了这种方法以优于遵守数据的现有方法。
ABIDE is the largest open-source autism spectrum disorder database with both fMRI data and full phenotype description. These data were extensively studied based on functional connectivity analysis as well as with deep learning on raw data, with top models accuracy close to 75\% for separate scanning sites. Yet there is still a problem of models transferability between different scanning sites within ABIDE. In the current paper, we for the first time perform domain adaptation for brain pathology classification problem on raw neuroimaging data. We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
