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Unsupervised learning can discover various unseen diseases, relying onlarge-scale unannotated medical images of healthy subjects. Towards this,unsupervised methods reconstruct a single medical image to detect outlierseither in the learned feature space or from high reconstruction loss. However,without considering continuity between multiple adjacent slices, they cannotdirectly discriminate diseases composed of the accumulation of subtleanatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study hasshown how unsupervised anomaly detection is associated with disease stages.Therefore, we propose a two-step method using Generative AdversarialNetwork-based multiple adjacent brain MRI slice reconstruction to detect AD atvarious stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1loss---trained on 3 healthy slices to reconstruct the next 3ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss(e.g., L2 loss) per scan discriminates them, comparing the reconstructed/groundtruth images. The results show that we can reliably detect AD at a very earlystage with Area Under the Curve (AUC) 0.780 while also detecting AD at a latestage much more accurately with AUC 0.917; since our method is fullyunsupervised, it should also discover and alert any anomalies including raredisease.

GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis