Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions. Among unsupervised methods, patch-based auto-encoders with their efficient representation power provided by their latent space, have shown good results for visible lesion detection. However, the commonly used reconstruction error criterion may limit their performance when facing less obvious lesions. In this work, we design two alternative detection criteria. They are derived from multivariate analysis and can more directly capture information from latent space representations. Their performance compares favorably with two additional supervised learning methods, on a difficult de novo Parkinson Disease (PD) classification task.

翻译：基于神经网络的异常检测在临床应用中仍面临挑战，尤其是在监督信息匮乏且出现如脑部损伤难以察觉等细微异常的情况下。在无监督方法中，基于图像块的自动编码器凭借其潜在空间提供的强大表征能力，在可见病灶检测中展现出良好效果。然而，常用的重建误差标准在处理不明显病变时可能限制其性能。本文设计了两种替代检测准则，它们源于多元分析，能够更直接地捕捉潜在空间表征中的信息。在一项具有挑战性的新发帕金森病分类任务中，该方法的性能优于两种附加的监督学习方法。