Yet another PyTorch implementation of the CheXNet algorithm for pathology detection in frontal chest X-ray images. This implementation is based on approach presented here. Ten-crops technique is used to transform images at the testing stage to get better accuracy.
The highest accuracy 0.8779 was achieved by the model m-30012020-104001.pth.tar (see the models directory). If you cannot download model weights download from here.
The same training (70%), validation (10%) and testing (20%) datasets were used as in this implementation.
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set up conda env python 3.6
conda create -n chexnet python=3.6 conda activate chexnet -
Pytorch
conda install pytorch==1.1.0 torchvision cudatoolkit=9.0 -c pytorch -
OpenCV (for generating CAMs)
conda install -c menpo opencv -
Pillow
pip install Pillow==6.1 -
sklearn
conda install -c anaconda scikit-learn -
pandas
conda install -c anaconda pandas
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Download the ChestX-ray14 database from here
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Unpack archives in separate directories copy all subdirectories images to database/xrays/images
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To run test/train by setting appropriate variables values in Main.py
python Main.py -
Use the runTrain() function in the Main.py to train a model from scratch
This implementation allows to conduct experiments with 3 different densenet architectures: densenet-121, densenet-169 and densenet-201.
- To generate CAM of a test file run script HeatmapGenerator
The highest accuracy 0.8779 was achieved by the model m-30012020-104001.pth.tar (see the models directory). If you cannot download model weights download from here.
| Pathology | AUROC |
|---|---|
| Atelectasis | 0.8333 |
| Cardiomegaly | 0.9434 |
| Effusion | 0.7848 |
| Infiltration | 0.9050 |
| Mass | 0.8628 |
| Nodule | 0.9614 |
| Pneumonia | 0.9138 |
| Pneumothorax | 0.9857 |
| Consolidation | 0.7665 |
| Edema | 0.9005 |
| Emphysema | 0.8514 |
| Fibrosis | 0.8507 |
| P Thickening | 0.7936 |
| Hernia | 0.9383 |
| AUROC mean | 0.8779 |