Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories

• URL: http://arxiv.org/abs/2507.23411v1
• Date: Thu, 31 Jul 2025 10:36:58 GMT
• Title: Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
• Authors: Lemar Abdi, Francisco Caetano, Amaan Valiuddin, Christiaan Viviers, Hamdi Joudeh, Fons van der Sommen,
• Abstract summary: In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases.<n>Current generative approaches often rely on likelihood estimation or reconstruction error.<n>We propose a reconstruction-free OOD detection method that leverages the forward diffusion trajectories of a Stein score-based denoising diffusion model.
• Score: 8.591748099943167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases with extremely low incidence rates. In contrast to supervised methods, OOD-based approaches function without labels and are inherently robust to data imbalances. Current generative approaches often rely on likelihood estimation or reconstruction error, but these methods can be computationally expensive, unreliable, and require retraining if the inlier data changes. These limitations hinder their ability to distinguish nominal from anomalous inputs efficiently, consistently, and robustly. We propose a reconstruction-free OOD detection method that leverages the forward diffusion trajectories of a Stein score-based denoising diffusion model (SBDDM). By capturing trajectory curvature via the estimated Stein score, our approach enables accurate anomaly scoring with only five diffusion steps. A single SBDDM pre-trained on a large, semantically aligned medical dataset generalizes effectively across multiple Near-OOD and Far-OOD benchmarks, achieving state-of-the-art performance while drastically reducing computational cost during inference. Compared to existing methods, SBDDM achieves a relative improvement of up to 10.43% and 18.10% for Near-OOD and Far-OOD detection, making it a practical building block for real-time, reliable computer-aided diagnosis.

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