https://github.com/HiLab-git/CA-Net

Code for Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation.
https://github.com/HiLab-git/CA-Net

attention-mechanism

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

• Host: GitHub
• URL: https://github.com/HiLab-git/CA-Net
• Owner: HiLab-git
• Created: 2020-08-27T09:34:21.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-01-20T07:48:22.000Z (almost 4 years ago)
• Last Synced: 2024-11-05T12:15:24.592Z (about 1 month ago)
• Topics: attention-mechanism
• Language: Python
• Homepage:
• Size: 37.3 MB
• Stars: 168
• Watchers: 3
• Forks: 39
• Open Issues: 10
• Metadata Files:
  • Readme: README.md

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• awesome_medical - CA-Net

README

        
## 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][paper_link]. Our work is accepted by [TMI][tmi_link].

[paper_link]:https://arxiv.org/pdf/2009.10549.pdf

[tmi_link]:https://ieeexplore.ieee.org/document/9246575

![mg_net](./pictures/canet_framework.png)

Fig. 1. Structure of CA-Net.

![uncertainty](./pictures/skin_results.png)

Fig. 2. Skin lesion segmentation.

![refinement](./pictures/fetal_mri_results.png)

Fig. 3. Placenta and fetal brain segmentation.

### Requirementss

Some important required packages include:

* [Pytorch][torch_link] version >=0.4.1.

* Visdom

* Python == 3.7 

* Some basic python packages such as Numpy.

Follow official guidance to install [Pytorch][torch_link].

[torch_link]:https://pytorch.org/

## Usages

### For skin lesion segmentation

1. First, you can download the dataset at [ISIC 2018][data_link]. We only used ISIC 2018 task1 training dataset, To preprocess the dataset and save as ".npy", run:

[data_link]:https://challenge.isic-archive.com/data#2018

```

python isic_preprocess.py 

```

2. For conducting 5-fold cross-validation, split the preprocessed data into 5 fold and save their filenames. run:

```

python create_folder.py 

```

2. 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

```

3. 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](./pictures/skin_segmentation_results_table.png)

4. 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](./pictures/spatial_atten_weight.png)

Visualzation of scale attention weight map:

![refinement](./pictures/scale_atten_weight.png)

## 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][AG].

[AG]:https://github.com/ozan-oktay/Attention-Gated-Networks