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--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- en |
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task_categories: |
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- object-detection |
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- text-generation |
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- zero-shot-classification |
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- image-to-text |
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task_ids: |
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- open-domain-qa |
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- image-captioning |
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- multi-class-classification |
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- multi-label-classification |
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- multi-input-text-classification |
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multimodal: |
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- image |
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- text |
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tags: |
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- vision-language |
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- open-world |
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- rare-diseases |
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- brain-mri |
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- medical-imaging |
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- out-of-distribution |
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- generalization |
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- anomaly-localization |
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- captioning |
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- diagnostic-reasoning |
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- zero-shot |
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--- |
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<table> |
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<tr> |
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<td><img src="https://huggingface.co/datasets/c-i-ber/Nova/resolve/main/logo.png" alt="NOVA Logo" width="40"></td> |
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<td><h1 style="margin:0; padding-left: 10px;">NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI</h1></td> |
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</tr> |
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</table> |
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> **An open-world generalization benchmark under clinical distribution shift** |
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[](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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[Dataset on 🤗 Hugging Face](https://huggingface.co/datasets/c-i-ber/Nova) |
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*For academic, non-commercial use only* |
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--- |
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## 🔖 Citation |
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If you find this dataset useful in your work, please consider citing it: |
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```bibtex |
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@article{bercea2025nova, |
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title={NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI}, |
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author={Bercea, Cosmin I. and Li, Jun and Raffler, Philipp and Riedel, Evamaria O. and Schmitzer, Lena and Kurz, Angela and Bitzer, Felix and Roßmüller, Paula and Canisius, Julian and Beyrle, Mirjam L. and others}, |
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journal={arXiv preprint arxiv:2505.14064}, |
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year={2025}, |
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note={Preprint. Under review.} |
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} |
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``` |
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[Paper](https://huggingface.co/papers/2505.14064) |
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[Evaluation Scripts](https://huggingface.co/c-i-ber/Nova_Evaluation) |
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--- |
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## 📦 Usage |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("parquet", data_files=f"hf://datasets/c-i-ber/Nova/data/nova-v1.parquet", split="train") |
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``` |
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## 🧪 Try it out in Colab |
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You can explore the NOVA dataset directly in your browser using the interactive notebook below: |
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[](https://huggingface.co/datasets/c-i-ber/Nova/blob/main/notebooks/nova_demo.ipynb) |
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Or open the notebook directly: |
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👉 [example.ipynb](https://huggingface.co/datasets/c-i-ber/Nova/blob/main/notebooks/nova-demo.ipynb) |
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--- |
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## 💡 Motivation |
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Machine learning models in real-world clinical settings must detect and reason about anomalies they have **never seen during training**. Current benchmarks mostly focus on known, curated categories—collapsing evaluation back into a **closed-set** problem and overstating model robustness. |
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**NOVA** is the first benchmark designed as a **zero-shot, evaluation-only** setting for assessing how well models: |
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- Detect **rare, real-world anomalies** |
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- Generalize across diverse MRI protocols and acquisition settings |
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- Perform **multimodal reasoning** from image, text, and clinical context |
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It challenges foundation models and vision-language systems with what they *were not trained for*: the **unexpected**. |
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--- |
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## 🧠 Dataset Overview |
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- **906 brain MRI slices** |
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- **281 rare neurological conditions**, spanning neoplastic, vascular, metabolic, congenital, and other pathologies |
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- **Real-world clinical heterogeneity** (unprocessed, long-tailed distribution) |
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- **Radiologist-written captions** and **double-blinded bounding boxes** |
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- **Clinical histories** and diagnostics for reasoning tasks |
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🛠️ All cases are **2D PNG slices**, sized 480×480, and are available under CC BY-NC-SA 4.0. |
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--- |
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## 📊 Benchmark Tasks |
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NOVA captures the clinical diagnostic workflow through **three open-world tasks**: |
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### 🔍 1. Anomaly Localization |
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Detect abnormal regions via bounding box prediction. Evaluated with: |
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- `mAP@30`, `mAP@50`, `mAP@[50:95]` |
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- True/false positive counts per case |
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### 📝 2. Image Captioning |
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Generate structured radiology-style descriptions. |
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- Evaluated with Clinical/Modality F1, BLEU, METEOR |
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- Also assesses normal/abnormal classification |
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### 🧩 3. Diagnostic Reasoning |
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Predict the correct diagnosis based on clinical history + image caption. |
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- Evaluated with Top-1, Top-5 accuracy |
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- Label coverage and entropy analysis for long-tail reasoning |
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--- |
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## 🧪 Model Performance (Stress Test) |
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| Task | Top Model (2025) | Top Metric | Score | |
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|-----------------------|--------------------|---------------------|----------| |
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| Anomaly Localization | Qwen2.5-VL-72B | mAP@50 | 24.5% | |
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| Image Captioning | Gemini 2.0 Flash | Clinical Term F1 | 19.8% | |
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| Diagnostic Reasoning | GPT-4o | Top-1 Accuracy | 24.2% | |
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Even top-tier foundation models fail under this **open-world generalization** benchmark. |
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--- |
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## ⚠️ Intended Use |
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NOVA is intended **strictly for evaluation**. Each case has a unique diagnosis, preventing leakage and forcing **true zero-shot testing**. |
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Do **not** fine-tune on this dataset. |
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Ideal for: |
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- Vision-language model benchmarking |
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- Zero-shot anomaly detection |
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- Rare disease generalization |
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--- |
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## 📬 Contact |
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Stay tuned for the **public leaderboard** coming soon. |
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--- |
