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--- |
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license: cc-by-nc-nd-4.0 |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- medical |
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size_categories: |
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- 1K<n<10K |
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--- |
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# U2-BENCH: Ultrasound Understanding Benchmark |
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**U2-BENCH** is the **first large-scale benchmark for evaluating Large Vision-Language Models (LVLMs) on ultrasound imaging understanding**. It provides a diverse, multi-task dataset curated from **40 licensed sources**, covering **15 anatomical regions** and **8 clinically inspired tasks** across classification, detection, regression, and text generation. |
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### Check the 🌟Leaderboard🌟here: https://dolphin-sound.github.io/u2-bench/ |
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### Evaluation code released! |
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Two options: |
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1. developed from VLMEvalKit: https://github.com/dolphin-sound/u2-bench-evalkit |
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2. developed by our intern Yalun: https://github.com/gurenolun/Dolphin-ai-bench |
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--- |
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## 📂 Dataset Structure |
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The dataset is organized into **8 folders**, each corresponding to one benchmark task: |
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- `caption_generation/` |
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- `clinical_value_estimation/` |
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- `disease_diagnosis/` |
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- `keypoint_detection/` |
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- `lesion_localisation/` |
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- `organ_detection/` |
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- `report_generation/` |
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- `view_recognition_and_assessment/` |
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Each folder contains `.tsv` files with task-specific annotations. A shared file, [`an_explanation_of_the_columns.tsv`](./an_explanation_of_the_columns.tsv), maps each column to its meaning. |
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--- |
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## 📄 Data Format |
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The dataset is provided as `.tsv` files, where: |
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- `img_data` contains a **base64-encoded image** (typically a 2D frame from an ultrasound video). |
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- Each row corresponds to a **single sample**. |
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- Columns include task-specific fields such as: |
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- `dataset_name`, `anatomy_location`, `classification_task` |
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- `caption`, `report`, `class_label`, `measurement`, `gt_bbox`, `keypoints`, etc. |
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A full explanation is provided in [`an_explanation_of_the_columns.tsv`](./an_explanation_of_the_columns.tsv). |
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--- |
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## 🔬 Tasks |
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U2-BENCH includes 8 core tasks: |
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| Capability | Task Name | Description | |
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|----------------|------------------------------|-------------------------------------------------| |
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| Classification | Disease Diagnosis (DD) | Predict clinical diagnosis from ultrasound | |
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| Classification | View Recognition (VRA) | Classify standard views in sonography | |
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| Detection | Lesion Localization (LL) | Locate lesions with spatial classification | |
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| Detection | Organ Detection (OD) | Identify presence of anatomical structures | |
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| Detection | Keypoint Detection (KD) | Predict anatomical landmarks (e.g. biometry) | |
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| Regression | Clinical Value Estimation | Estimate scalar metrics (e.g., fat %, EF) | |
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| Generation | Report Generation | Produce structured clinical ultrasound reports | |
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| Generation | Caption Generation | Generate brief anatomical image descriptions | |
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--- |
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## 📊 Dataset Statistics |
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- **Total samples**: 7,241 |
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- **Anatomies**: 15 (e.g., thyroid, fetus, liver, breast, heart, lung) |
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- **Application scenarios**: 50 across tasks |
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- **Multi-task support**: Some samples contain multiple labels (e.g., classification + regression) |
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--- |
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## 🛡️ Ethics, License & Use |
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- The dataset is distributed under the **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)** license. |
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- For **non-commercial research and evaluation only**. |
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- Data is derived from **licensed and publicly available ultrasound datasets**. |
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- All images are de-identified, and annotations were manually validated. |
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- **Do not use** this dataset for diagnostic or clinical deployment without regulatory approval. |
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--- |
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## 📦 Loading from Hugging Face |
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You can load the dataset using 🤗 Datasets: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("DolphinAI/u2-bench", split="train") |
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``` |
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--- |
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## 📚 Citation |
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If you use this benchmark in your research, please cite: |
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```bibtex |
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@article{le2025u2bench, |
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title={U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding}, |
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author={Le, Anjie and Liu, Henan and others}, |
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journal={Under Review}, |
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year={2025} |
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} |
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``` |
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--- |
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## 🔧 Contributions |
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We welcome community contributions and evaluation scripts. |
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Please open a pull request or contact Dolphin AI for collaboration. |
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