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

• URL: http://arxiv.org/abs/2505.14064v1
• Date: Tue, 20 May 2025 08:10:57 GMT
• Title: NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
• Abstract summary: In many real-world applications, deployed models encounter inputs that differ from the data seen during training.<n>We present $NOVA$, a challenging, real-life $sim$900 brain MRI scans that span 281 rare pathologies and heterogeneous acquisition protocols.<n>Each case includes rich clinical narratives and double-blinded expert bounding-box annotations.<n>Because NOVA is never used for training, it serves as an $extreme$ stress-test of out-of-distribution generalisation.
• Score: 11.644236197583405
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems from an unseen distribution, while open-world recognition flags such inputs to ensure the system remains 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 across all tasks, establishing NOVA as a rigorous testbed for advancing models that can detect, localize, and reason about truly unknown anomalies.

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