I have a dataset with medical images int16. I have tried to calculate mean and std to use Normalize.
Like this
mean_tr = (train_x.float().mean()/10209)
std_tr = (train_x.float().std()/10209)
mean_te = (test_x.float().mean()/11108)
std_te = (test_x.float().std()/11108)
Where 10209 is the value of the biggest pixel in train, and 11108 in test.
I have something like this :
mean_tr = tensor(0.0439) std_tr = tensor(0.0616)
mean_te = tensor(0.0425) std_te = tensor(0.0586)
I then used this values to Normalize data, with tranform, and I print some values to see what happened. I saw values like 60000, after this step, very big values, and smalest was -0.7. Why? Can you help me with some ideas how to deal with this images?
Thanks
Do you use 3D medical images?
Hi,
Did you perform the transformation transform.functional.to_tensor() firstly? Because it will scale your data in range[0, 1] and then perform normalization on it, you could have a try.
This images are 2D slices from 3D medical images, from what I understand T1.
In documentation, it says that will converts PIL Images, or numpy array that are in range [0, 255].
No one have an answer?
Hi @David_Jitca,
Maybe you can use the preprocessing transforms in TorchIO. Also, sharing a minimal working example would help us help you.