UniTransAD: Unified Translation Framework for Anomaly Detection in Brain MRI.

Abstract

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: (1) a unified cyclic-translation inference paradigm built upon content-style disentanglement, capable of processing diverse brain MRI inputs; (2) a Dynamic Style Prototype Memory (DSPM) that enables a flexible and robust cyclic-inference mechanism; and (3) a dual-level detection mechanism that combines pixel-level translation errors with feature-level dissimilarities to enhance detection specificity. Furthermore, to rigorously evaluate generalization beyond disease-specific datasets, we establish the Brain-OmniA evaluation dataset, 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. In summary, UniTransAD offers a robust, flexible and generalizable solution for clinical anomaly detection in heterogeneous clinical environments. Our code, pre-trained models, and full dataset are available at: https://github.com/zhibaishouheilab/UniTransAD.