Fibro-CoSANet: Pulmonary Fibrosis Prognosis Prediction using a Convolutional Self Attention Network

Installation

1. git clone https://github.com/zabir-nabil/osic-pulmonary-fibrosis-progression.git
2. cd osic-pulmonary-fibrosis-progression
3. Install Anaconda Anaconda
4. conda create -n pulmo python==3.7.5
5. conda activate pulmo
6. conda install -c intel mkl_fft (opt.)
7. conda install -c intel mkl_random (opt.)
8. conda install -c anaconda mkl-service (opt.)
9. pip install -r requirements.txt

Download Dataset

1. Download the kaggle.json from Kaggle account. Kaggle authentication
2. Keep the kaggle.json file inside data_download folder.
3. sudo mkdir /root/.kaggle
4. sudo cp kaggle.json /root/.kaggle/
5. sudo apt install unzip if not installed already

• cd data_download; python dataset_download.py; mv osic-pulmonary-fibrosis-progression.zip ../../; unzip ../../osic-pulmonary-fibrosis-progression.zip -d ../../; cd ../; python train_slopes.py

Training

1. Set the training hyperparameters in config.py
2. Slope Prediction
   • To train slopes model run python train_slopes.py
   • trained model weights and results will be saved inside hyp.results_dir
3. Quantile Regression
   • To train qreg model run python train_qreg.py
   • trained model weights and results will be saved inside hyp.results_dir

• Volume calculation: https://www.kaggle.com/furcifer/q-regression-with-ct-tabular-features-pytorch

Arxiv pre-print

https://arxiv.org/abs/2104.05889