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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},
}
``` |