HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection

• URL: http://arxiv.org/abs/2606.22556v1
• Date: Sun, 21 Jun 2026 15:26:48 GMT
• Title: HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection
• Abstract summary: HiMatch-AD is a DINOv3-driven hierarchical matching framework for training-free medical anomaly detection.<n>We introduce a unified uncertainty-based fusion mechanism that adaptively weights maps according to their reliability.<n>Experiments on the BMAD benchmark, including brain MRI, liver CT, and retinal OCT datasets, demonstrate that HiMatch-AD consistently outperforms both training-based and DINO-based state-of-the-art methods.
• Score: 6.789086317935452
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Anomaly detection is essential for medical image analysis, where pathological regions often appear as rare deviations from normal anatomical structures. While training-based methods have achieved promising performance, they require task-specific optimization and extensive normal data, which limits scalability across modalities and institutions. Training-free approaches offer greater flexibility by leveraging pretrained visual representations, yet existing methods typically rely on simple nearest-neighbor retrieval and naive aggregation strategies, which may fail to capture hierarchical semantics and ignore the reliability of multiple anomaly responses. In this work, we propose HiMatch-AD, a DINOv3-driven hierarchical matching framework for training-free medical anomaly detection. Our method first retrieves semantically relevant normal references via dual-branch matching that jointly considers global CLS-token similarity and patch-level representations. Hierarchical anomaly maps are then generated across multiple transformer stages by comparing clustered normal features with query representations. To robustly aggregate anomaly responses, we introduce a unified uncertainty-based fusion mechanism that adaptively weights maps according to their reliability. The entire framework operates without any task-specific training. Extensive experiments on the BMAD benchmark, including brain MRI, liver CT, and retinal OCT datasets, demonstrate that HiMatch-AD consistently outperforms both training-based and DINO-based state-of-the-art methods, which highlights the effectiveness of multi-level matching and uncertainty-aware fusion for scalable medical anomaly detection.

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