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UA-GAN

Uncertainty and Attention based GAN for multimodal image translation

Environment

python              3.8.10
pytorch             1.8.1
torchvision         0.9.1
tqdm                4.62.1
numpy               1.20.3
SimpleITK           2.1.0
scikit-learn        0.24.2
opencv-python       4.5.3.56
easydict            1.9
tensorboard         2.5.0
Pillow              8.3.1

Datasets

Download the datasets from the official way and rearrange the files to the following structure. The dataset path can be modified in the UA-GAN/options/*.yaml file.

BraTS2020

MICCAI_BraTS2020_TrainingData
├── flair
│   ├── BraTS20_Training_001_flair.nii.gz
│   ├── BraTS20_Training_002_flair.nii.gz
│   ├── BraTS20_Training_003_flair.nii.gz
│   ├── ...
├── t2
│   ├── BraTS20_Training_001_t2.nii.gz
│   ├── BraTS20_Training_002_t2.nii.gz
│   ├── BraTS20_Training_003_t2.nii.gz
│   ├── ...
├── t1
│   ├── BraTS20_Training_001_t1.nii.gz
│   ├── BraTS20_Training_002_t1.nii.gz
│   ├── BraTS20_Training_003_t1.nii.gz
│   ├── ...
├── t1ce
│   ├── BraTS20_Training_001_t1ce.nii.gz
│   ├── BraTS20_Training_002_t1ce.nii.gz
│   ├── BraTS20_Training_003_t1ce.nii.gz
│   ├── ...

RaFD

For RaFD dataset, you just need to follow the original dataset with the image name like 'Rafd090_01_Caucasian_female_angry_frontal.jpg'. We only select the images shooting from frontal direction (Rafd090) for training and testing.

Train

Edit the .yaml file of the corresponding dataset for training configuration and run the following command to train our model.

python train.py options/brats.yaml

Test

Edit the .yaml file of the corresponding dataset for testing configuration and run the following command to test.

python test.py options/brats.yaml