A Weakly Supervised Adaptive Densenet for Classifying Thoracic Diseases and Identifying Abnormalities

Bo Zhou, Yuemeng Li, Jiangcong Wang

[Paper]

This repository contains the PyTorch implementation of adaptive densenet for chest x-ray's weakly supervised learning.

Citation

If you use this code for your research or project, please cite:

@article{zhou2018weakly,
  title={A weakly supervised adaptive densenet for classifying thoracic diseases and identifying abnormalities},
  author={Zhou, Bo and Li, Yuemeng and Wang, Jiangcong},
  journal={arXiv preprint arXiv:1807.01257},
  year={2018}
}

Environment and Dependencies

Requirements:

• Python 3.7
• Pytorch 0.4.1
• scipy
• scikit-image
• opencv-python
• tqdm

Our code has been tested with Python 3.7, Pytorch 0.4.1, CUDA 10.0 on Ubuntu 18.04.

Dataset Setup

../
Data/
ChestXray14
├── images                   # contain all the 1024x1024 imaging data in .png format
│   ├── 00000001_000.png         
│   ├── 00000001_001.png 
│   ├── ...         
│   └── 00030805_000.png 
│
├── labels                   # contain train / val / test .txt label splitted files
│   ├── train_list.txt         
│   ├── val_list.txt      
│   └── test_list.txt 
│            
└── ...

Each .png is an image data with intensity ranged between 0~255.

Please download the ChestXray14 dataset from LINK.

To Run Our Code

• Train the model

python train.py --experiment_name 'train_ChestXray14_densenetADA' --model_type 'model_wsl' --dataset 'ChestXray14' --data_root '../Data/ChestXray14/' --net_G 'densenetADA' --n_class 14 --batch_size 36 --lr 1e-4 --eval_epochs 4 --save_epochs 4 --snapshot_epochs 4 --AUG --gpu_ids 0

where
--experiment_name provides the experiment name for the current run, and save all the corresponding results under the experiment_name's folder.
--data_root provides the data folder directory (with structure illustrated above).
--AUG adds for using data augmentation option (rotation, random cropping, scaling).
Other hyperparameters can be adjusted in the code as well.

• Test the model

python test.py --resume './outputs/train_ChestXray14_densenetADA/checkpoints/model_best.pt' --experiment_name 'test_ChestXray14_densenetADA' --model_type 'model_wsl' --data_root '../Data/ChestXray14/' --net_G 'densenetADA' --gpu_ids 0

where
--resume defines which checkpoint for testing and evaluation. The 'model_best.pt' can be generated by training the model.
The test will output an eval.mat containing model's prediction and weakly supervised heatmaps for evaluation in the '--experiment_name' folder.

Sample training/test scripts are provided under './scripts/' and can be directly executed.

Contact

If you have any question, please file an issue or contact the author:

Bo Zhou: bo.zhou@yale.edu