meddlr

Meddlr is a config-driven ML framework built to simplify medical image reconstruction and analysis problems.

⚡ QuickStart

# Install Meddlr with basic dependencies
pip install meddlr

# Install Meddlr with all dependencies (e.g. pretrained models, benchmarking)
pip install 'meddlr[all]'

Installing locally: For local development, fork and clone the repo and run pip install -e ".[alldev]"

Installing from main: For most up-to-date code without a local install, run pip install "meddlr @ git+https://github.com/ad12/meddlr@main"

Configure your paths and get going!

import meddlr as mr
import os

# (Optional) Configure and save machine/cluster preferences.
# This only has to be done once and will persist across sessions.
cluster = mr.Cluster()
cluster.set(results_dir="/path/to/save/results", data_dir="/path/to/datasets")
cluster.save()
# OR set these as environment variables.
os.environ["MEDDLR_RESULTS_DIR"] = "/path/to/save/results"
os.environ["MEDDLR_DATASETS_DIR"] = "/path/to/datasets"

Detailed instructions are available in Getting Started.

Visualizations

Use MeddlrViz to visualize your medical imaging datasets, ML models, and more!

pip install meddlr-viz

A gallery of images from the BRATS dataset

🐘 Model Zoo

Easily serve and download pretrained models from the model zoo. A (evolving) list of pre-trained models can be found here, on HuggingFace 🤗, and in project folders.

To use them, pass the URLs for the config and weights (model) files to mr.get_model_from_zoo:

import meddlr as mr

model = mr.get_model_from_zoo(
  cfg_or_file="https://huggingface.co/arjundd/vortex-release/resolve/main/mridata_knee_3dfse/Supervised/config.yaml",
  weights_path="https://huggingface.co/arjundd/vortex-release/resolve/main/mridata_knee_3dfse/Supervised/model.ckpt",
)

📓 Projects

Check out some projects built with meddlr!

✏️ Contributing

Want to add new features, fix a bug, or add your project? We'd love to include them! See CONTRIBUTING.md for more information.

Acknowledgements

Meddlr's design for rapid experimentation and benchmarking is inspired by detectron2.

About

If you use Meddlr for your work, please consider citing the following work:

@article{desai2021noise2recon,
  title={Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising},
  author={Desai, Arjun D and Ozturkler, Batu M and Sandino, Christopher M and Vasanawala, Shreyas and Hargreaves, Brian A and Re, Christopher M and Pauly, John M and Chaudhari, Akshay S},
  journal={arXiv preprint arXiv:2110.00075},
  year={2021}
}

anqin888/meddlr