Provides Python code to reproduce model training, predictions, and heatmaps from the CheXNet paper that predicted 14 common diagnoses using convolutional neural networks in over 100,000 NIH chest x-rays.
Click on the launch binder button at the top of this README to launch a remote instance in your browser using binder. This requires no local configuration, but it can take a couple minutes to launch. Open Explore_Predictions.ipynb, run all cells, and follow the instructions provided to review a selection of included chest x-rays from NIH.
To configure your own local instance (assumes Anaconda is installed; can be run on paperspace GPU instance if you do not have a GPU):
cd reproduce-chexnet
conda env create -f environment.yml
source activate reproduce-chexnet
python -m ipykernel install --user --name reproduce-chexnet --display-name "Python (reproduce-chexnet)"
Changes in libraries available through conda channels and non-compability of older versions of pytorch / torchvision with newer CUDA drivers caused the original build to break.
This library was updated in late 2022 so anyone interested could continue to use it. (1) environment.yml was updated and slight edits to code were made to ensure compatibility with newer version of pytorch/torchvision (2) NIH CXR labels were updated to latest version. Given changes in torchvision model naming conventions and updated labels, a new model was retrained; reported AUC numbers are based on this retrained model.
Given expected variability in predictions of retrained deep learning models, predictions vary from the model originally posted in 2018. The original 2018 model and predictions are shared in a /pretrained-old folder, but will require you to create a compatible environment with the older pytorch 0.4.0 and torchvision 0.2.0 to use them interactively.
This reproduction achieved diagnosis-level AUC as given below compared to original paper:
| retrained auc | chexnet auc | |
|---|---|---|
| label | ||
| Atelectasis | 0.8180 | 0.8094 |
| Cardiomegaly | 0.9090 | 0.9248 |
| Consolidation | 0.8002 | 0.7901 |
| Edema | 0.8945 | 0.8878 |
| Effusion | 0.8827 | 0.8638 |
| Emphysema | 0.9316 | 0.9371 |
| Fibrosis | 0.8251 | 0.8047 |
| Hernia | 0.9175 | 0.9164 |
| Infiltration | 0.7156 | 0.7345 |
| Mass | 0.8377 | 0.8676 |
| Nodule | 0.7756 | 0.7802 |
| Pleural_Thickening | 0.7889 | 0.8062 |
| Pneumonia | 0.7617 | 0.7680 |
| Pneumothorax | 0.8776 | 0.8887 |
aucs.csv: test AUCs of retrained model vs original ChexNet reported resultscheckpoint: saved model checkpointlog_train: log of train and val loss by epochpreds.csv: individual probabilities for each finding in each test set image predicted by retrained model
To explore the full dataset, download images from NIH (large, ~40gb compressed),
extract all tar.gz files to a single folder, and provide path as needed in code. You can use batch download script provided by NIH researchers included in this repo:
python nih_batch_download_zips.py
Please note: a GPU is required to train the model. You will encounter errors if you do not have a GPU available and compatible CUDA installed and you attempt to retrain. With a GPU, you can retrain the model with retrain.py. Make sure you download the full NIH dataset before trying this. If you run out of GPU memory, reduce BATCH_SIZE from its default setting of 16.
Please ensure your CUDA driver is compatible with the CUDA toolkit (v11.3) installed by default.
If you do not have a GPU, but wish to retrain the model yourself to verify performance, you can replicate the model with paperspace, Amazon EC2, Google Colaboratory, or other online cloud GPU services. If you're starting from scratch, paperspace is easy to get started with.
I use SGD+momentum rather than the Adam optimizer as described in the original paper. I achieved better results with SGD+momentum, as has been reported in other work.
A sample of 621 test NIH chest x-rays enriched for positive pathology is included with the repo to faciliate immediate use and exploration in the Explore Predictions.ipynb notebook. The full NIH dataset is required for model retraining.
My goal in releasing this code is to increase transparency and replicability of deep learning models in radiology. I encourage you to use this code to start your own projects. If you do, please cite the repo:
author = {Zech, J.},
title = {reproduce-chexnet},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jrzech/reproduce-chexnet}}
}
With deep gratitude to researchers and developers at PyTorch, NIH, Stanford, and Project Jupyter, on whose generous work this project relies. With special thanks to Sasank Chilamkurthy, whose demonstration code was incorporated into this project. PyTorch is an incredible contribution to the research community.