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This dataset is based on the BraTS2023 dataset.
It takes 5 middle slices from each nifti volume of the BraTS2023 dataset after normalizing to a value of (-1,1).
All of these images are .npy files and one can load them using the np.load(FILEPATH).astype(np.float32).
We provide the training and the test set which contains 6255 and 1095 files respectively.
It is highly recommend to create a separate validation set from the training dataset for applications.
We use Pytorch to do this. We do this by using the following command.
seed = 97
train_dataset, val_dataset = torch.utils.data.random_split(
dataset, lengths=(0.9, 0.1), generator=torch.Generator().manual_seed(seed)
) # dataset is the dataset instance.
This dataset is actually part of a paper which is under peer-review currently. It is mainly used for multi-domain medical image to image translation.
We hope this helps the community.
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