Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate. While powerful data generation mechanisms, such as Generative Adversarial Networks (GANs), have been designed in the last decade for computer vision, such improvements have not yet carried over to brain imaging. A likely reason is that GANs training is ill-suited to the noisy, high-dimensional and small-sample data available in functional neuroimaging. In this paper, we introduce Conditional Independent Components Analysis (Conditional ICA): a fast functional Magnetic Resonance Imaging (fMRI) data augmentation technique, that leverages abundant resting-state data to create images by sampling from an ICA decomposition. We then propose a mechanism to condition the generator on classes observed with few samples. We first show that the generative mechanism is successful at synthesizing data indistinguishable from observations, and that it yields gains in classification accuracy in brain decoding problems. In particular it outperforms GANs while being much easier to optimize and interpret. Lastly, Conditional ICA enhances classification accuracy in eight datasets without further parameters tuning.
翻译:计算认知神经成像研究的进展与大量贴有标签的脑成像数据的可用性有关,但这类数据稀缺而且产生费用昂贵。虽然过去十年中设计了强大的数据生成机制,如基因反转网络(GANs),用于计算机视觉,但这种改进尚未传到大脑成像上。一个可能的原因是,GANs培训不适合功能神经成像中可获取的噪音、高维度和小型抽样数据。在本文中,我们引入了条件独立部件分析(ICA):快速功能磁共振成像(FRI)数据增强技术,利用大量休息状态数据从ICA脱腐中取样生成图像。然后我们提出一个机制,使发电机在所观测到的班级上配备少量样品。我们首先显示,基因化机制成功地将数据与观测结果相容成可分解的数据合成,并在大脑解码问题中产生分解精度增益。特别是它超越了GANs的精确度,同时不易进行最优化和精确性地改进了GANs的精确度。