Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast. Patch-based representation learning has shown powerful representation capacities when applied to industrial or medical imaging and outlier detection methods have been applied successfully to these images. In this work, we propose an unsupervised anomaly detection (UAD) method based on a latent space constructed by a siamese patch-based auto-encoder and perform the outlier detection with a One-Class SVM training paradigm tailored to the lesion detection task in multi-modality neuroimaging. We evaluate performances of this model on a public database, the White Matter Hyperintensities (WMH) challenge and show in par performance with the two best performing state-of-the-art methods reported so far.

翻译：异常检测在神经影像中仍是一项具有挑战性的任务，尤其是在监督信息极少或缺失，且病灶可能非常小或对比度微弱的情况下。基于图像块的表示学习在工业或医学影像中展现出强大的表征能力，而异常点检测方法已成功应用于这些图像。本研究提出一种无监督异常检测方法，该方法基于由孪生图像块自编码器构建的潜空间，并采用针对多模态神经影像中病灶检测任务定制的一类支持向量机训练范式进行异常点检测。我们在公开数据库——白质高信号挑战赛上评估了该模型的性能，结果表明其与目前报道的两种最优前沿方法表现相当。