A flexible ML framework built to simplify medical image reconstruction and analysis experimentation.

Overview

meddlr

Getting Started

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

Installation

To avoid cuda-related issues, downloading torch, torchvision, and cupy (optional) must be done prior to downloading other requirements.

# Create and activate the environment.
conda create -n meddlr_env python=3.7
conda activate meddlr_env

# Install cuda-dependant libraries. Change cuda version as needed.
# Below we show examples for cuda-10.1
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
pip install cupy-cuda101

# Install as package in virtual environment (recommended):
git clone https://github.com/ad12/meddlr.git
cd dl-ss-recon && python -m pip install -e '.[dev]'

# For all contributors, install development packages.
make dev

Contributing

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}
}
Owner
Arjun Desai
Arjun Desai
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