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🛡️ Model Usage Agreement
By accessing or using this model (the “Model”), you acknowledge and agree to the following terms and conditions:
1. Research-Only Use
The Model is provided strictly for non-commercial, academic, and research purposes. It must not be used for clinical decision-making, diagnosis, treatment, or any other application involving real patients or clinical care.
2. No Clinical or Commercial Deployment
The Model is not approved for clinical use or any commercial application. Any deployment in healthcare settings or use for patient-related decision support is expressly prohibited.
3. Redistribution and Modification
You may not copy, distribute, sublicense, or otherwise share the Model or any derivative works without prior written permission from the model authors or the ODELIA consortium.
4. Privacy and Ethics Compliance
You must not attempt to identify, re-identify, or deanonymize any individual whose data may have contributed to the training or evaluation of the Model.
5. Attribution Requirement
Any publication, presentation, or derivative work that uses or references this Model must include clear attribution to the ODELIA consortium, along with any citations specified in the accompanying documentation.
6. Responsibility and Verification
You are solely responsible for verifying and validating the Model’s outputs and ensuring they are appropriate for your research context. The Model and its outputs are provided “as is,” without warranties of any kind.
7. Inclusion of Third-Party Components
This Model incorporates or is derived from DINOv3, developed by Meta Platforms. Use of the Model is therefore also subject to the DINOv3 License Agreement. By using this Model, you agree to comply with both:
- This Model Usage Agreement, and * The DINOv3 License Terms.
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ODELIA Classification Baseline Model
For a comprehensive description of the model and its intended use, please refer to our paper: Read the paper
Setup
To run the code, we recommend creating a Python virtual environment.
Using venv
# Create a virtual environment
python -m venv venv
# Activate the environment
# On Linux/Mac:
source venv/bin/activate
# On Windows:
# venv\Scripts\activate
# Install dependencies
pip install torch torchvision numpy huggingface_hub torchio matplotlib transformers einops x_transformers
Using Conda
# Create a conda environment
conda create -n odelia_hf python=3.10
conda activate odelia_hf
# Install dependencies
pip install torch torchvision numpy huggingface_hub torchio matplotlib transformers einops x_transformers
Get Probabilities and Attention
To use this model, first download the required files from this repository:
from huggingface_hub import hf_hub_download
# Download model files to local directory
hf_hub_download(repo_id="ODELIA-AI/MST", filename="models.py", local_dir="./")
hf_hub_download(repo_id="ODELIA-AI/MST", filename="predict_attention.py", local_dir="./")
Then execute predict_attention.py --path_img path/to/Sub_1.nii.gz to get probabilities and attention maps.