This repo is dedicated for our attempt in research using Standford's Musculoskeletal Radiographs (MURA) dataset to detect abnormality
bash setup.sh
conda activate mura
python main.py --env server --mode train
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>
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 App is developed in React.
To host the front end, change to the dir:
cd src/frontend
yarn
yarn start
* 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
@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}
}