UniTransAD: Unified Translation Framework for Anomaly Detection in Brain MRI

Unsupervised anomaly detection (UAD) in brain MRI is crucial for early diagnosis, yet generalizing existing methods across diverse diseases, sequences, and missing data scenarios remains a significant challenge. Current reconstruction-based methods often fail to detect subtle anomalies, while conventional translation methods lack flexibility regarding input sequences. To address these limitations, we propose UniTransAD, a unified translation-based anomaly detection framework. UniTransAD introduces three key innovations: a unified cyclic-translation inference paradigm built upon content-style disentanglement, a Dynamic Style Prototype Memory that enables flexible and robust cyclic inference, and a dual-level detection mechanism combining pixel-level translation errors with feature-level dissimilarities. We also establish the Brain-OmniA evaluation dataset by aggregating seven public datasets covering distinct brain pathologies and sequences. Extensive experiments demonstrate that UniTransAD significantly outperforms state-of-the-art methods on Brain-OmniA with superior flexibility.

Journal article

IEEE Transactions on Medical Imaging