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:在临床应用中,基于神经网络的异常现象的检测仍然具有挑战性,因为临床应用中几乎没有或根本没有受到监督的信息,以及难以察觉的脑损伤等微妙异常现象。在未受监督的方法中,利用潜在空间提供的有效代表力的基于补丁的自动校对器在可见的损伤检测中取得了良好结果。然而,通常使用的重建错误标准可能会限制其面对较不明显的损伤时的性能。在这项工作中,我们设计了两种替代的检测标准。它们来自多变量分析,可以更直接地获取潜在空间表现的信息。它们的性能优于另外两种受监督的学习方法,即难以避免的帕金森病(PD)分类任务。</s>