PANDA: Patch-based unsupervised deep learning for brain anomaly detection via age prediction in fetal MRI.

Unsupervised anomaly detection, which learns exclusively from normal cases to identify significant deviations from normative patterns without prior knowledge of specific anomaly types, offers a promising approach for automatic diagnosis of such conditions.

To enhance anomaly detection capabilities, this study introduces a three-dimensional (3D) Patch-based brain ANomaly Detection framework via Age prediction (PANDA), utilizing the maximum absolute age difference across all patches (MaxAAD) as a biomarker for identifying fetal brain anomalies.