Unsupervised Brain MRI Anomaly Detection for Multiple Sclerosis Classification

Supervised deep learning has been widely applied in medical imaging to detect multiple sclerosis. However, it is difficult to have perfectly annotated lesions in magnetic resonance images, due to the inherent difficulties with the annotation process performed by human experts. To provide a model that can completely ignore annotations, we propose an unsupervised anomaly detection approach. The method uses a convolutional autoencoder to learn a "normal brain" distribution and detects abnormalities as a deviation from the norm. Experiments conducted with the recently released OASIS-3 dataset and the challenging MSSEG dataset show the feasibility of the proposed method, as very encouraging sensitivity and specificity were achieved in the binary health/disease discrimination. Following the "normal brain" learning rule, the proposed approach can easily generalize to other types of brain diseases, due to its potential to detect arbitrary anomalies.