I am working on some medical data with a very limited dataset.
I have some MRI as training images and CT as the corresponding images that I want from MRI images (this is going to be a GAN network to make fake CTs with U-Net based generator).
I am coding the generator part and wanted to train my UNet to see whether it is working okay.
But I am confused on how to correctly load images; my issue is this:
I have more MRI images than CT. I am thinking maybe for every epoch, assuming each patient to be a batch, I want to load the maximum possible images at random from each patient and then train my UNet. How can I do this using PyTorch data.Dataset ? Do I need to have 1:1 MRI and CT images to train the network?
Thank you in advance!