NOVA: A Benchmark for Rare Anomaly Localization and Clinical Reasoning in Brain MRI

In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Open-world recognition ensures that such systems remain robust as ever-emerging, previously _unknown_ categories appear and must be addressed without retraining. Foundation and vision-language models are pre-trained on large and diverse datasets with the expectation of broad generalization across domains, including medical imaging. However, benchmarking these models on test sets with only a few common outlier types silently collapses the evaluation back to a closed-set problem, masking failures on rare or truly novel conditions encountered in clinical use. We therefore present NOVA, a challenging, real-life _evaluation-only_ benchmark of $\sim$900 brain MRI scans that span 281 rare pathologies and heterogeneous acquisition protocols. Each case includes rich clinical narratives and double-blinded expert bounding-box annotations. Together, these enable joint assessment of anomaly localisation, visual captioning, and diagnostic reasoning. Because NOVA is never used for training, it serves as an _extreme_ stress-test of out-of-distribution generalisation: models must bridge a distribution gap both in sample appearance and in semantic space. Baseline results with leading vision-language models (GPT-4o, Gemini 2. 0 Flash, and Qwen2. 5-VL-72B) reveal substantial performance drops, with approximately a 65\% gap in localisation compared to natural-image benchmarks and 40\% and 20\% gaps in captioning and reasoning, respectively, compared to resident radiologists. Therefore, NOVA establishes a testbed for advancing models that can detect, localize, and reason about truly unknown anomalies.

Authors

• Cosmin Bercea
• Jun Li
• Philipp Raffler
• Evamaria O. Riedel
• Lena Schmitzer
• Angela Kurz
• Felix Bitzer
• Paula Roßmüller
• Julian Canisius
• Mirjam Beyrle
• Che Liu
• Wenjia Bai
• Bernhard Kainz Friedrich-Alexander-Universität Erlangen-Nürnberg, DE Imperial College London, UK
• Julia Schnabel
• Benedikt Wiestler

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