/KAD

Primary LanguagePythonMIT LicenseMIT

KAD

The official codes for Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images.

Dependencies

To clone all files:

git clone

To install Python dependencies:

pip install -r requirements.txt

Note that the complete data file and model training logs/checkpoints can be download from link: https://pan.baidu.com/s/1Jokoeb6VCT1lvd9A1kNxog?pwd=m35f (m35f). and google drive with link: https://drive.google.com/drive/folders/1xWVVJRfnm_wIgUpbn9ftsW4K5XKU0i0-?usp=share_link.

Data

Training Dataset

  1. Navigate to MIMIC-CXR Database to download the training dataset. Note: in order to gain access to the data, you must be a credentialed user as defined on PhysioNet.

  2. preprocess MIMIC-CXR
    preprocess dataset into csv file as ./A1_DATA/MIMIC-CXR/data_file/caption.csv

cd ./A1_DATA/MIMIC-CXR/data_preprocess

sh run_preprocess.sh

Evaluation Dataset

1. PadChest Dataset

The PadChest dataset contains chest X-rays that were interpreted by 18 radiologists at the Hospital Universitario de San Juan, Alicante, Spain, from January 2009 to December 2017. The dataset contains 109,931 image studies and 168,861 images. PadChest also contains 206,222 study reports.

The PadChest is publicly available at https://bimcv.cipf.es/bimcv-projects/padchest. Those who would like to use PadChest for experimentation should request access to PadChest at the link.

2. NIH ChestXrat14 Dataset

NIH ChestXray14 dataset has 112,120 X-ray images with disease labels from 30,805 unique patients. Authors use natural language processing and the associated radiological reports to text-mine disease labels. There are 14 disease labels: Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia.

The dataset and official split can be obtain at https://nihcc.app.box.com/v/ChestXray-NIHCC

3. CheXpert Dataset

The CheXpert dataset consists of chest radiographic examinations from Stanford Hospital, performed between October 2002 and July 2017 in both inpatient and outpatient centers. Population-level characteristics are unavailable for the CheXpert test dataset, as they are used for official evaluation on the CheXpert leaderboard.

The main data (CheXpert data) supporting the results of this study are available at https://aimi.stanford.edu/chexpert-chest-x-rays.

The CheXpert test dataset has recently been made public, and can be found by following the steps in the cheXpert-test-set-labels repository.

Pre-training

1. Knowledge encoder

The UMLS knowledge base file used during pretraining is in ./A2_KEBERT/data

run the following command to perform Med-KEBERT pretraining

cd ./A2_KEBERT

python main.py --pretrained GanjinZero/UMLSBert_ENG --batch-size 256 --max_length 128 --logs logs --name medkgbert --output_dir --aws_output_dir

Arguments
  • --output_dir directory to save logs
  • --aws_output_dir directory to save checkpoints

2. Pre-trained Model

cd ./A3_CLIP

python main.py --use_entity_features --image_encoder_name resnet --bert_model_name xmcmic/Med-KEBERT --freeze_bert --bert_pretrained --output_dir --aws_output_dir

Arguments

  • --freeze_bert whether freeze bert

  • --bert_pretrained pertained Med-KEBERT model path

  • --output_dir directory to save logs

  • --aws_output_dir directory to save checkpoints

Zero-shot Inference

The img_res in configs need to be changed for different pre-trained checkpoints.

python test_chestxray14.py --image_encoder_name --bert_model_name xmcmic/Med-KEBERT --bert_pretrained --output_dir --checkpoint

python test_chexpert.py --image_encoder_name --bert_model_name xmcmic/Med-KEBERT --bert_pretrained --output_dir

python test_padchest.py --image_encoder_name --bert_model_name xmcmic/Med-KEBERT --bert_pretrained --output_dir --checkpoint

Arguments

  • --bert_pretrained pertained Med-KEBERT model path

  • --output_dir directory to save result csv files

  • --checkpoint directory of pre-trained model's checkpoints

  • --img_res the input image resolution, 512 for KAD-512 model and 1024 for KAD-1024, changed in the config file

Model Checkpoints

We provide the models' checkpoints for KAD-512 and KAD-1024, which can be download from https://pan.baidu.com/s/1Jokoeb6VCT1lvd9A1kNxog?pwd=m35f (m35f) or from google drive with link: https://drive.google.com/drive/folders/1xWVVJRfnm_wIgUpbn9ftsW4K5XKU0i0-?usp=share_link.

Citation

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

@article{zhang2023knowledge,
  title={Knowledge-enhanced visual-language pre-training on chest radiology images},
  author={Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Xie, Weidi and Wang, Yanfeng},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={4542},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

If you have any question, please feel free to contact.