Beyond One-Step Residuals: Trajectory-Aware Manifold Projection for Few-Shot Medical Anomaly Detection

Visual foundation models provide structured normal feature spaces for manifold-based anomaly detection. However, in few-shot medical settings, inter-individual variation induces train-test distribution shifts, leaving limited normal samples insufficient to characterize the normal manifold. Inspired by a dynamical systems perspective, we propose Trajectory-Aware Manifold Projection (TAMP), which exploits trajectory information beyond initial projection residuals. TAMP learns normal-manifold projectors through consistency-guided multi-level learning and repeatedly applies them during inference to construct recovery trajectories. We observe that initial residuals capture deviations, while subsequent projection steps reveal different responses to normal variations and lesions. Using consecutive projection displacements as additional anomaly cues, we introduce the post-projection trajectory ratio and terminal convergence residual to measure relative continued changes after the initial projection and the remaining final-step update, respectively. These complement the initial residual for anomaly scoring. Experiments on multiple medical benchmarks demonstrate improved detection and localization over existing few-shot methods, with competitive results against full-shot unsupervised methods using only a few normal references.