Unsupervised Anomaly Detection by Learning Elastic Transformations Within an Autoencoder Approach

Machine learning has approached magnetic resonance image (MRI) analysis using multiple techniques, including deep supervised learning methodologies, where lesions such as tumors or features associated with defined pathologies have been identified satisfactorily. However, many of these models require a certain amount of labeled data which access is usually restricted, due to the protection of health information. Furthermore, those methodologies are focused only on the pathologies considered in the training process, causing the model to ignore other kinds of brain lesions. In this paper, an unsupervised anomaly detection methodology is developed to model healthy structures and detect anomaly regions in MRI. Random elastic transform patches are applied over the whole volume to quantify and optimize the reconstruction performance during training, leading to effective anomaly region detection. The experimental results show competitive results with common state-of-the-art unsupervised approaches.