README.md

Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment

基于自学习知识库和多模态对其机制的医学报告生成

Requirements

• Python >= 3.6
• Pytorch >= 1.7
• torchvison
• Microsoft COCO Caption Evaluation Tools
• CheXpert

conda activate tencent

Data

Download IU and MIMIC-CXR datasets, and place them in data folder.

• IU dataset from here
• MIMIC-CXR dataset from here

Folder Structure

• config : setup training arguments and data path
• data : store IU and MIMIC dataset
• models: basic model and all our models
• modules:
  • the layer define of our model
  • dataloader
  • loss function
  • metrics
  • tokenizer
  • some utils
• pycocoevalcap: Microsoft COCO Caption Evaluation Tools

Training and Testing

• The validation and testing will run after training.
• More options can be found in config/opts.py file.
• The model will be trained using command：
  • $dataset_name:
    • iu: IU dataset
    • mimic: MIMIC dataset

  1. full model

     python main.py --cfg config/{$dataset_name}_resnet.yml --expe_name {$experiment name} --label_loss --rank_loss --version 12
     

  2. basic model

     python main_basic.py --cfg config/{$dataset_name}_resnet.yml --expe_name {$experiment name} --label_loss --rank_loss --version 91
     

  3. our model without the learned knowledge base

     python main.py --cfg config/{$dataset_name}_resnet.yml --expe_name {$experiment name} --label_loss --rank_loss --version 92
     

  4. for the model without multi-modal alignment You remove --label_loss or --rank_loss from the commonds.

Citation

Shuxin Yang, Xian Wu, Shen Ge, ZhuoZhao Zheng, S. Kevin Zhou, Li Xiao,Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment. Medical Image Analysis,2023

Contact

If you have any problem with the code, please contact Shuxin Yang([email protected]) or Li Xiao([email protected]).