Unsupervised Out-of-Distribution (OOD) detection consists in identifying
anomalous regions in images leveraging only models trained on images of healthy
anatomy. An established approach is to tokenize images and model the
distribution of tokens with Auto-Regressive (AR) models. AR models are used to
1) identify anomalous tokens and 2) in-paint anomalous representations with
in-distribution tokens. However, AR models are slow at inference time and prone
to error accumulation issues which negatively affect OOD detection performance.
Our novel method, MIM-OOD, overcomes both speed and error accumulation issues
by replacing the AR model with two task-specific networks: 1) a transformer
optimized to identify anomalous tokens and 2) a transformer optimized to
in-paint anomalous tokens using masked image modelling (MIM). Our experiments
with brain MRI anomalies show that MIM-OOD substantially outperforms AR models
(DICE 0.458 vs 0.301) while achieving a nearly 25x speedup (9.5s vs 244s).

使用两个与任务相关的 Transformer，通过应用 MIM 模型来替代 AR 模型，实现对脑部 MRI 异常的快速检测和修复，明显优于 AR 模型，并获得近 25 倍的加速。