Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images

The automatic detection of anomalies in medical images is a significant challenge in the assisted diagnosis of neurodegenerative diseases such as Alzheimer's. This paper presents an anomaly detection model based on Transformers for the analysis of brain magnetic resonance images. The proposed architecture combines a Vision Transformer as an encoder with a memory bank module that allows modeling the distribution of healthy brains and detecting deviations through reconstruction error. The model is trained using a one-class learning approach, using only images considered normal, with the aim of learning the representation of normality and automatically flagging atypical structural patterns. To adapt volumetric studies to the architecture, a preprocessing procedure is designed that transforms three-dimensional information into a two-dimensional representation compatible with the model. The results obtained demonstrate a solid ability to characterize normality and generate reliable predictions, confirming the viability of Transformer-based architectures for unsupervised anomaly detection in neuroimaging. This approach lays the foundation for future extensions in clinical settings and other medical imaging applications.