Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Cosmin I. Bercea Benedikt Wiestler Daniel Rueckert Julia A. Schnabel
The increasing complexity of medical imaging data underscores the need for
advanced anomaly detection methods to automatically identify diverse
pathologies. Current methods face challenges in capturing the broad spectrum of
anomalies, often limiting their use to specific lesion types in brain scans. To
address this challenge, we introduce a novel unsupervised approach, termed
\textit{Reversed Auto-Encoders (RA)}, designed to create realistic
pseudo-healthy reconstructions that enable the detection of a wider range of
pathologies. We evaluate the proposed method across various imaging modalities,
including magnetic resonance imaging (MRI) of the brain, pediatric wrist X-ray,
and chest X-ray, and demonstrate superior performance in detecting anomalies
compared to existing state-of-the-art methods. Our unsupervised anomaly
detection approach may enhance diagnostic accuracy in medical imaging by
identifying a broader range of unknown pathologies. Our code is publicly
available at: \url{https://github.com/ci-ber/RA}.