metadata
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 and 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
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 paper:
@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},
}