整个MILC：跨任务、数据集和人群概括学习到的动态
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
摘要：
行为变化是精神障碍的最早迹象，但可以说，大脑功能的动态甚至更早受到影响。因此，疾病特异性动力学的时空结构对于早期诊断和理解疾病机制至关重要。学习区分性特征的一种常见方法依赖于训练分类器和评估特征重要性。基于手工特征的经典分类器非常强大，但当应用于大输入维度的时空数据时，会遭受维度灾难。深度学习算法可以处理这个问题，模型内省可以突出有区别的时空区域，但需要更多的样本来进行训练。在本文中，我们提出了一种新的自我监督的训练模式，加强整个序列互信息本地的上下文（整个MILC）。我们在未标记和不相关的健康对照数据上预训练整个MILC模型。我们在三种不同的疾病（i）精神分裂症（ii）自闭症和（iii）阿尔茨海默病和四项不同的研究中测试我们的模型。我们的算法优于现有的自监督预训练方法，并提供了有竞争力的分类结果，经典的机器学习算法。重要的是，整个MILC能够将受试者诊断归因于fMRI信号中的特定时空区域。
英文摘要：
Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
