Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging

TL;DR

WALDO introduces a zero-shot, reference-based localisation framework for medical imaging that leverages optimal transport and structured comparison to improve anomaly detection accuracy.

Contribution

It presents a training-free, optimal transport-based method that enhances zero-shot localisation by structured reference comparison and introduces the Goldilocks zone sampling strategy.

Findings

Achieves 43.5% mAP@30 on NOVA brain MRI benchmark, a 19% improvement over baselines.

Demonstrates consistent gains across different vision-language models.

Statistically significant improvements confirmed by McNemar tests.

Abstract

Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is fundamentally limited by the absence of healthy anatomical context. We reformulate zero-shot localisation as a comparative inference problem in which anomalies are identified through structured comparison against reference distributions of normal anatomy. We introduce WALDO, a training-free framework grounded in optimal transport theory that enables comparative reasoning through: (i) entropy-weighted Sliced Wasserstein distances for anatomically-aware reference selection from DINOv2 patch distributions, (ii) Goldilocks zone sampling exploiting the non-monotonic relationship between reference similarity and localisation accuracy, and (iii) self-consistency aggregation via weighted non-maximum suppression. We theoretically analyse the Goldilocks effect through distributional divergence, and show that references with moderate similarity minimize a bias-variance trade-off in comparative visual reasoning. On the NOVA brain MRI benchmark, WALDO with Qwen2.5-VL-72B achieves $43.5_{\pm1.6}\%$ mAP@30 (95\% CI: [40.4, 46.7]), representing a 19\% relative improvement over zero-shot baselines. Cross-model evaluation shows consistent gains: GPT-4o achieves $32.0_{\pm6.5}\%$ and Qwen3-VL-32B achieves $32.0_{\pm6.6}\%$ mAP@30. Paired McNemar tests confirm statistical significance ($p<0.01$). Source code is available at https://github.com/bkainz/WALDO_MICCAI26_demo .