Catching MRI outliers: unsupervised detection and localization of MRI artefacts and clinical anomalies using deep learning
Researchers have developed an unsupervised deep learning framework to detect and localize anomalies in MRI scans, aiming to improve radiotherapy workflows. The two-stage system first tokenizes MRI slices and then models the distribution of normal tokens to identify deviations. This approach demonstrated high accuracy, with AUCs of 0.97 for pelvic MRI and 0.81 for brain MRI, and showed strong spatial agreement for anomaly localization. AI
IMPACT Enhances AI reliability in medical imaging by providing a quality control layer for radiotherapy workflows.