3rd place solution for the 2018 RSNA Pneumonia Detection Challenge
by Phillip Cheng, MD MS
Hello! Below is an outline of how I prepared the data, trained my models, and predicted bounding boxes for the RSNA Pneumonia Detection Challenge. Further details are in the model documentation submitted to Kaggle. For questions please contact me at email@example.com
data: contains raw and processed data from Kaggle
keras-retinanet: modified version of keras-retinanet
models: selected resnet-50 and resnet-101 model snapshots used for the prediction submissions
- Note: the .h5 model files are too large to be stored in the Github repository, so you will need to manually download the .h5 files from the release and place them in this folder.
snapshots: folder for saved snapshots after each epoch of training
submissions: folder to hold output from
- Dell Precision T5500 with 3.07 GHz Intel Xeon X5675 CPU
- 12 GB NVIDIA Titan Xp GPU
- 512 GB HDD
- 16 GB DDR3 RAM
(python packages are detailed separately in
- Ubuntu 18.04 LTS
- Python 3.6.6
- CUDA 9.0
- cuDNN 7.0.5
- NVIDIA driver v.390
Data and keras-retinanet setup
(assumes the Kaggle API is installed and configured)
Run the following shell commands from the top level directory
cd data kaggle competitions download -c rsna-pneumonia-detection-challenge unzip stage_2_test_images.zip -d stage_2_test_images unzip stage_2_train_images.zip -d stage_2_train_images cd ../keras-retinanet python setup.py build_ext --inplace cd .. python prepare_data.py
settings.json is configured for creating training and validation sets from the Stage 1 training labels, which are what I used to train the models for the competition. I did not perform any training on the Stage 2 training set, which included Stage 1 test images.
There are two RetinaNets used in my solution, which are trained by the following scripts.
Snapshots after each epoch of training are saved in
snapshots. For the competition, I manually selected the best snapshots from several runs based on the precision score and maximum Youden index reported during training.
Bounding box prediction
Output .csv is saved to