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

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📂 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, maps each column to its meaning.

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📄 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.

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🔬 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

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📊 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)

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🛡️ 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.

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📦 Loading from Hugging Face

You can load the dataset using 🤗 Datasets:

from datasets import load_dataset

dataset = load_dataset("DolphinAI/u2-bench", split="train")

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📚 Citation

If you use this benchmark in your research, please cite:

@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}
}

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🔧 Contributions

We welcome community contributions and evaluation scripts. Please open a pull request or contact Dolphin AI for collaboration.