基于孪生神经网络潜在空间的单类SVM用于脑MRI白质高信号的无监督异常检测
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities
AI总结：
本文提出一种基于孪生图像块自编码器潜在空间与单类SVM的无监督异常检测方法，用于多模态脑MRI白质高信号检测，在WMH挑战赛上取得与当前最优方法相当的性能。
摘要：
在几乎没有监督且病灶可能非常小或对比度微弱的情况下，异常检测在神经影像中仍然是一项具有挑战性的任务。基于图像块的表示学习在工业或医学影像应用中已展现出强大的表示能力，并且离群点检测方法已成功应用于这些图像。在本工作中，我们提出了一种无监督异常检测（UAD）方法，该方法基于由孪生图像块自编码器构建的潜在空间，并使用针对多模态神经影像中病灶检测任务而定制的单类SVM训练范式进行离群点检测。我们在公共数据库——白质高信号（WMH）挑战赛上评估了该模型的性能，并展示其与迄今为止报道的两个性能最佳的最先进方法相当的表现。
英文摘要：
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.
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