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Dataset Summary
This dataset is based on the BraTS2023 dataset and is supposed to be used for Multi-domain Image-to-Image Translation task. 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 for each domain. These are actually 4 domains, and are named accordingly.
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.
We hope this helps the community.
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