task_categories:image-to-image language:en license:mit region:us medical
---
license: mit
task_categories:
- image-to-image
language:
- en
tags:
- medical
---

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.

```python
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.
