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Anomaly detection remains a challenging task in neuroimaging when little tono supervision is available and when lesions can be very small or with subtlecontrast. Patch-based representation learning has shown powerful representationcapacities when applied to industrial or medical imaging and outlier detectionmethods have been applied successfully to these images. In this work, wepropose an unsupervised anomaly detection (UAD) method based on a latent spaceconstructed by a siamese patch-based auto-encoder and perform the outlierdetection with a One-Class SVM training paradigm tailored to the lesiondetection task in multi-modality neuroimaging. We evaluate performances of thismodel on a public database, the White Matter Hyperintensities (WMH) challengeand show in par performance with the two best performing state-of-the-artmethods reported so far.

One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities