README.md

---
license: apache-2.0
tags:
- ultrasound
- YOLOv11
base_model:
- chaoyinshe/EchoVLM_V2_lingshu_base_7b_instruct_preview
- chaoyinshe/EchoVLM-Thyroid-9B
---

# EchoVLM Ultrasound Anonymization Tool
This repository provides an ultrasound image anonymization toolkit built based on **YOLOv11**.
It acts as the official supporting preprocessing tool for **[EchoVLM](https://huggingface.co/chaoyinshe/EchoVLM_V2_lingshu_base_7b_instruct_preview)** and **[EchoVLM-Thyroid-9B](https://huggingface.co/chaoyinshe/EchoVLM-Thyroid-9B)**.
The tool automatically detects and obscures sensitive text labels, patient identifiers and identifiable markers on ultrasound images to meet medical data privacy compliance requirements.

## Environment Requirements
```bash
pip install ultralytics
from ultralytics import YOLO

# Load trained YOLOv11 detection model for anonymization
model = YOLO("yolo11.pt")  

# Run batch inference on thyroid ultrasound images
results = model(\["image1.jpg", "image2.jpg"\])  # Return a list of Results objects

# Process detection results and perform anonymization
for result in results:
    boxes = result.boxes          # Bounding boxes of sensitive regions
    masks = result.masks          # Segmentation masks (for segmentation models)
    keypoints = result.keypoints  # Keypoints object
    probs = result.probs          # Classification probabilities
    obb = result.obb              # Oriented bounding boxes for rotated text

    result.show()                 # Visualize detection results
    result.save(filename="result.jpg")  # Save visualized detection output
    # Implement blurring or masking logic on detected regions to generate anonymized images
```
## Citation
If you use this tool in your research or project, please cite our [EchoVLM](https://arxiv.org/abs/2509.14977) paper:
```bash
@misc{she2026echovlmdynamicmixtureofexpertsvisionlanguage,
      title={EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence}, 
      author={Chaoyin She and Ruifang Lu and Lida Chen and Wei Wang and Qinghua Huang},
      year={2026},
      eprint={2509.14977},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.14977}, 
}
```