基于自学习知识库和多模态对其机制的医学报告生成
Python >= 3.6
Pytorch >= 1.7
torchvison
conda activate tencent
Download IU and MIMIC-CXR datasets, and place them in data
folder.
config/opts.py
file.The model will be trained using command:
$dataset_name:
python main.py --cfg config/{$dataset_name}_resnet.yml --expe_name {$experiment name} --label_loss --rank_loss --version 12
basic model
python main_basic.py --cfg config/{$dataset_name}_resnet.yml --expe_name {$experiment name} --label_loss --rank_loss --version 91
our model without the learned knowledge base
python main.py --cfg config/{$dataset_name}_resnet.yml --expe_name {$experiment name} --label_loss --rank_loss --version 92
for the model without multi-modal alignment
You remove --label_loss
or --rank_loss
from the commonds.
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
If you have any problem with the code, please contact Shuxin Yang(aspenstarss@gmail.com) or Li Xiao(andrew.lxiao@gmail.com).