This project is a tool to build CheXpert-like models, written in Keras.

CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets.

1. Train/test a baseline model by following the quickstart. You can get a model with performance close to the paper.
2. Run class activation mapping to see the localization of your model.
3. Modify multiply parameter in config.ini or design your own class weighting to see if you can get better performance.
4. Modify weights.py to customize your weights in loss function. If you find something useful, feel free to make that an option and fire a PR.
5. Every time you do a new experiment, make sure you modify output_dir in config.ini otherwise previous training results might be overwritten. For more options check the parameter description in config.ini.

Note that currently this project can only be executed in Linux and macOS. You might run into some issues in Windows.

1. Download all tar files, train.csv and valid.csv of CheXpert dataset from Stanford Mirror. Put them under ./data/default_split folder and untar all tar files.
2. Create & source a new virtualenv. Python >= 3.6 is required.
3. Install dependencies by running pip3 install -r requirements.txt.
4. Copy sample_config.ini to config.ini, you may customize batch_size and training parameters here. Make sure config.ini is configured before you run training or testing
5. Run python train.py to train a new model. If you want to run the training using multiple GPUs, just prepend CUDA_VISIBLE_DEVICES=0,1,... to restrict the GPU devices. nvidia-smi command will be helpful if you don't know which device are available.
6. Run python test.py to evaluate your model on the test set.

If you use >= CUDA 9, make sure you set tensorflow_gpu >= 1.5.

Bruce Chou ([email protected])

Ali Gholami ([email protected])

MIT