论文标题
通过时间逆转的自我监督精神障碍分类器
Self-Supervised Mental Disorder Classifiers via Time Reversal
论文作者
论文摘要
数据稀缺是一个明显的问题，尤其是由于患者数据法而在医疗领域中。因此，有效的预训练技术可以帮助解决这个问题。在本文中，我们证明了一个在功能神经成像数据的时间方向上训练的模型可以帮助完成任何下游任务，例如，在fMRI数据中对健康控制的疾病进行分类。我们使用独立组件分析（ICA）技术来训练一个深度神经网络，以从fMRI数据得出的独立组件上进行训练。它在基于ICA的数据中学习时间方向。该预训练的模型进一步训练，以对不同数据集中的脑疾病进行分类。通过各种实验，我们已经表明，学习时间方向有助于模型在fMRI数据中学习一些因果关系，这有助于更快地收敛，因此，该模型在下游分类任务中即使具有更少的数据记录也可以很好地推广。
Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. It learns time direction in the ICA-based data. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we have shown that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence, and consequently, the model generalizes well in downstream classification tasks even with fewer data records.
