MA
mahendrathapa/MURA
Research project using Standford's Musculoskeletal Radiographs (MURA) dataset to detect Abnormality
This repo is dedicated for our attempt in research using Standford's Musculoskeletal Radiographs (MURA) dataset to detect abnormality
Set up conda environment
bash setup.sh
Activate conda environment
conda activate mura
Train Model
python main.py --env server --mode train
Inference from Model
python main.py --env server --mode predict --run_id <unique identifier> --model_checkpoint <saved model present inside out/<run_id>/checkpoints> --predict_data_dir <image dir for inference>
Back-End: Run the Flask Server
Flask serves the trained model through API, before serving, we need to update the RUN_ID and MODEL_NAME in src/constant.py
python -m src.api
Front-End
Front-End App is developed in React.
To host the front end, change to the dir:
cd src/frontend
Install React Dependency
yarn
Run Front-End
yarn start
Our Trained Model
* Download link for latest model:
- https://drive.google.com/drive/folders/1pnME9FFNsNJ8WJcuofn33PbExCN-XzzM?usp=sharing
* Create necessary folder structure: out/<run_id>/checkpoints
* Move the downloaded model inside: out/<run_id>/checkpoints
Citation
@ARTICLE{2017arXiv171206957R,
author = {{Rajpurkar}, Pranav and {Irvin}, Jeremy and {Bagul}, Aarti and
{Ding}, Daisy and {Duan}, Tony and {Mehta}, Hershel and {Yang}, Brand
on and {Zhu}, Kaylie and {Laird}, Dillon and {Ball}, Robyn L. and
{Langlotz}, Curtis and {Shpanskaya}, Katie and {Lungren}, Matthew P. and
{Ng}, Andrew Y.},
title = "{MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs}",
journal = {arXiv e-prints},
keywords = {Physics - Medical Physics, Computer Science - Artificial Intelligence},
year = "2017",
month = "Dec",
eid = {arXiv:1712.06957},
pages = {arXiv:1712.06957},
archivePrefix = {arXiv},
eprint = {1712.06957},
primaryClass = {physics.med-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv171206957R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}