Early screening is essential for effective intervention and treatment of individuals with mental disorders. Functional magnetic resonance imaging (fMRI) is a noninvasive tool for depicting neural activity and has demonstrated strong potential as a technique for identifying mental disorders. Due to the difficulty in data collection and diagnosis, imaging data from patients are rare at a single site, whereas abundant healthy control data are available from public datasets. However, joint use of these data from multiple sites for classification model training is hindered by cross-domain distribution discrepancy and diverse label spaces. Herein, we propose few-shot domain-adaptive anomaly detection (FAAD) to achieve cross-site anomaly detection of brain images based on only a few labeled samples. We introduce domain adaptation to mitigate cross-domain distribution discrepancy and jointly align the general and conditional feature distributions of imaging data across multiple sites. We utilize fMRI data of healthy subjects in the Human Connectome Project (HCP) as the source domain and fMRI images from six independent sites, including patients with mental disorders and demographically matched healthy controls, as target domains. Experiments showed the superiority of the proposed method compared with binary classification, traditional anomaly detection methods, and several recognized domain adaptation methods.

中文翻译：

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跨站点大脑图像的少样本域自适应异常检测


早期筛查对于精神障碍患者的有效干预和治疗至关重要。功能性磁共振成像（fMRI）是一种描述神经活动的非侵入性工具，并已显示出作为识别精神障碍的技术的强大潜力。由于数据收集和诊断的困难，单个站点的患者影像数据很少，而公共数据集中有丰富的健康对照数据。然而，跨域分布差异和不同标签空间阻碍了联合使用来自多个站点的这些数据进行分类模型训练。在这里，我们提出了少样本域自适应异常检测（FAAD），以仅基于少量标记样本来实现大脑图像的跨站点异常检测。我们引入域适应来减轻跨域分布差异，并共同调整多个站点之间成像数据的一般特征和条件特征分布。我们利用人类连接组计划 (HCP) 中健康受试者的 fMRI 数据作为源域，并将来自六个独立站点的 fMRI 图像（包括精神障碍患者和人口统计匹配的健康对照）作为目标域。实验表明，与二元分类、传统异常检测方法和几种公认的领域适应方法相比，该方法具有优越性。