ChestXNet

Chest X-ray Disease Diagnosis by Samarth Keshari, Gaurav Bhatt, Vivek Vishal

Installation

The software is built using pytohn 3.8.5 and uses following packages

focal-loss-torch>=0.0.7
pandas>=1.2.3
Pillow>=8.1.2
scikit-learn>=0.23.2
torch>=1.7.0
urllib3>=1.26.4

You can automatically install all of these packages by first cloning the repo https://github.com/samarthkeshari/CS598-ChestXNet.git. Then navigate into the project directory and run pip install -r requirements.txt.

Usage

There are three APIs that you can use.

Data Acquisition

cd src

The purpose of this API is to download the sample Chest X-ray images from NIH website. This API will download 12 .tar files from NIH website in ../data/ also extract the images from the tar files in ../data/images

To run, python data_acquisition.py

Model Training

cd src

The purpose of this API is to start the Model Training. It can be controlled using parameters that are maintained in file train_params.json. If required, change the parameters file.

The trained model weights will be saved after each epoch in ./models or the path specified in custom parameters file as epoch#-modelname.pth.

The model metrics loss, roc-auc will get saved in ./metrics or the path specified in custom parameters file as modelname-TRAIN-LOSS.csv.csv and modelname-TRAIN-CLASS-AUC.csv.

If a validation run is required during training, then set the parameter "perform_validation":"True" in the custom parameters json file. The validation metrics will get saved in ./metrics or the path specified in custom parameters file as modelname-VAL-LOSS.csv and modelname-VAL-CLASS-AUC.csv .

To run, python model_training.py <modelname ('VGG16' or 'VGG16-ATTN')> <custom parameters json file(optional)>

Note: Runinng training and validation together will take lot of time, so it is advised to run validation seperately using the Model Testing API as described below

Model Testing

cd src

The purpose of this API is to start the Model Validation. It can be controlled using parameters that are maintained in file test_params.json. If required, change the parameters file.

The validation metrics will get saved in ./metrics or the path specified in custom parameters file as modelname-VAL-LOSS.csv and modelname-VAL-CLASS-AUC.csv.

To run, python model_testing.py <modelname <trained-model-filename> <custom parameters json file(optional)>

Important Point

• The dataset size is about 45 GB, so training and vaidation must be done using GPU. It took around 6 hours to train the model with 1 GPU.
• The code supports training using only 1 GPU