Skip to content
master
Switch branches/tags
Code

Files

Permalink
Failed to load latest commit information.

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

This repository provides the code for "CA-Net: Comprehensive attention Convolutional Neural Networks for Explainable Medical Image Segmentation". Our work now is available on Arxiv. Our work is accepted by TMI.

mg_net Fig. 1. Structure of CA-Net.

uncertainty Fig. 2. Skin lesion segmentation.

refinement

Fig. 3. Placenta and fetal brain segmentation.

Requirementss

Some important required packages include:

  • Pytorch version >=0.4.1.
  • Visdom
  • Python == 3.7
  • Some basic python packages such as Numpy.

Follow official guidance to install Pytorch.

Usages

For skin lesion segmentation

  1. First, you can download the dataset at ISIC 2018. We only used ISIC 2018 task1 training dataset, To preprocess the dataset and save as ".npy", run:
python isic_preprocess.py 
  1. For conducting 5-fold cross-validation, split the preprocessed data into 5 fold and save their filenames. run:
python create_folder.py 
  1. To train CA-Net in ISIC 2018 (taking 1st-fold validation for example), run:
python main.py --data ISIC2018 --val_folder folder1 --id Comp_Atten_Unet
  1. To evaluate the trained model in ISIC 2018 (we added a test data in folder0, testing the 0th-fold validation for example), run:
python validation.py --data ISIC2018 --val_folder folder0 --id Comp_Atten_Unet

Our experimental results are shown in the table: refinement

  1. You can save the attention weight map in the middle step of the network to '/result' folder. Visualizing the attention weight above the original images, run:
python show_fused_heatmap.py

Visualzation of spatial attention weight map: refinement

Visualzation of scale attention weight map: refinement

Citation

If you find our work is helpful for your research, please consider to cite:

@article{gu2020net,
  title={CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation},
  author={Gu, Ran and Wang, Guotai and Song, Tao and Huang, Rui and Aertsen, Michael and Deprest, Jan and Ourselin, S{\'e}bastien and Vercauteren, Tom and Zhang, Shaoting},
  journal={IEEE Transactions on Medical Imaging},
  year={2020},
  publisher={IEEE}
}

Acknowledgement

Part of the code is revised from Attention-Gate-Networks.

About

Code for Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation.

Topics

Resources

Releases

No releases published

Packages

No packages published

Languages