Update README.md

README.md

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 - medical
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 - 1K<n<10K
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+---
+
+# 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.
+
+---
+
+## 📂 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.