The best approach is to write a custom Dataset, something like:
from torch.utils.data import Dataset, DataLoader

# custom dataset
class MRIData(Dataset):
    def __init__(self, test=False):
        # List of all image files
        self.patients = os.listdir('./data')
        
    def __len__(self):
        return len(self.patients)
    
    def __getitem__(self, x):
        patient = self.patients[x]
        filename = './data/'+ patient
        image = np.load(filename, allow_pickle=True)

        images = image[0][0]
        images = torch.from_numpy(images)
        label = image[0][1].item()
        label = torch.tensor(abs(label), dtype=torch.float)
        
        return images, label

train_ds = MRIData()
train_dl = DataLoader(train_ds, batch_size=1..) # add whatever args you need

You can try this by changing path to data