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