The fork has been made in the scope of an academic project "Machine Learning in Medical Image Processing"

Main contributions are complex augmentations and the MobileNet2.

The json files were adapted for training on Google Colab, since it was the platform we trained the models.

Top1 Solution of CheXpert

What is Chexpert?

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.

Why Chexpert?

Chest radiography is the most common imaging examination globally, critical for screening, diagnosis, and management of many life threatening diseases. Automated chest radiograph interpretation at the level of practicing radiologists could provide substantial benefit in many medical settings, from improved workflow prioritization and clinical decision support to large-scale screening and global population health initiatives. For progress in both development and validation of automated algorithms, we realized there was a need for a labeled dataset that (1) was large, (2) had strong reference standards, and (3) provided expert human performance metrics for comparison.

How to take part in?

CheXpert uses a hidden test set for official evaluation of models. Teams submit their executable code on Codalab, which is then run on a test set that is not publicly readable. Such a setup preserves the integrity of the test results.

Here's a tutorial walking you through official evaluation of your model. Once your model has been evaluated officially, your scores will be added to the leaderboard.Please refer to the https://stanfordmlgroup.github.io/competitions/chexpert/

What the code include?

If you want to train yourself from scratch, we provide training and test the footwork code. In addition, we provide complete training courses
If you want to use our model in your method, we provide a best single network pre-training model, and you can get the network code in the code

Train the model by yourself

Data preparation

We gave you the example file, which is in the folder config/train.csv You can follow it and write its path to config/example.json

If you want to train the model,please run the command. (We use 4 1080Ti for training, so larger than 4 gpus is recommended）:

pip install -r requirements.txt

python Chexpert/bin/train.py Chexpert/config/example.json logdir --num_workers 8 --device_ids "0,1,2,3"

If you want to test your model, please run the command:

cd logdir/

Cuz we set "save_top_k": 3 in the config/example.json, so we may have got 3 models for ensemble here. So you should do as below:

cp best1.ckpt best.ckpt

python classification/bin/test.py

If you want to plot the roc figure and get the AUC, please run the command

python classification/bin/roc.py plotname

How about drink a cup of coffee?

you can run the command like this. Then you can have a cup of caffe.(log will be written down on the disk) python Chexpert/bin/train.py Chexpert/config/example.json logdir --num_workers 8 --device_ids "0,1,2,3" --logtofile True &

train the model with pre-trained weights

We provide one pre-trained model here: config/pre_train.pth we test it on 200 patients dataset, got the AUC as below:

Cardiomegaly	Edema	Consolidation	Atelectasis	Pleural_Effusion
0.8703	0.9436	0.9334	0.9029	0.9166

You can train the model with pre-trained weights, run the command as below:

python Chexpert/bin/train.py Chexpert/config/example.json logdir --num_workers 8 --device_ids "0,1,2,3" --pre_train "Chexpert/config/pre_train.pth"

Plot heatmap using trained model

Currently supported global_pool options in /config/example.json to plot heatmaps

global_pool	Support
MAX	Yes
AVG	Yes
EXP	Yes
LSE	Yes
LINEAR	Yes
PCAM	Yes
AVG_MAX	No
AVG_MAX_LSE	No

We also provide heatmap comparision here, including AVG, LSE, and our own PCAM pooling.

	original	AVG (dev mAUC:0.895)	LSE (dev mAUC:0.896)	PCAM (dev mAUC:0.896)
Cardiomegaly
Atelectasis
Pleural Effusion
Consolidation

You can plot heatmaps using command as below:

python Chexpert/bin/heatmap.py logdir/best1.ckpt logdir/cfg.json CheXper_valid.txt logdir/heatmap_Cardiomegaly/ --device_ids '0' --prefix 'Cardiomegaly'

Where the CheXper_valid.txt contains lines of jpg path

About PCAM pooling

PCAM Overview:

If you think PCAM is a good way to generate heatmaps, you can cite our article like this:

Citation

@misc{ye2020weakly,
    title={Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling},
    author={Wenwu Ye and Jin Yao and Hui Xue and Yi Li},
    year={2020},
    eprint={2005.14480},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Contact

If you have any quesions, please post it on github issues or email at coolver@sina.com

Reference

mitalbert/Chexpert

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