论文标题
通过有条件的自动编码器和对抗性学习来识别阿尔茨海默氏病中的大脑功能障碍的规范建模
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
论文作者
论文摘要
规范建模是一种有效研究单个参与者的异质性异质性的新兴而有希望的方法。在这项研究中，我们提出了一种新颖的规范建模方法，通过将条件变异自动编码器与对抗性学习（ACVAE）相结合，以鉴定阿尔茨海默氏病（AD）中的脑功能障碍。具体而言，我们首先在健康对照组（HC）组上训练有条件的VAE，以创建以年龄，性别和颅内体积等协变量为条件的规范模型。然后，我们结合了一个对抗性训练过程，以构建一个可以更好地概括为看不见数据的歧视性特征空间。最后，我们计算与患者水平正常标准的偏差，以确定哪些大脑区域与AD相关。我们对OASIS-3数据库的实验表明，与其他深层规范模型相比，我们模型产生的偏差图对AD的敏感性更高，并且能够更好地识别AD和HC组之间的差异。
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.
