I'm getting literally crazy trying to understand how U-NET works. Maybe it is very easy, but I'm stuck (and I have a terrible headache). So, I need your help.
I'm going to segment MRI to find white matter hyperintensities. I have a dataset with MRI brain images, and another dataset with the WMH. For each one of the brain images, I have one black image with white dots on it in the WMH dataset. These white dots represent where is a WMH on its corresponding brain image.
This is an image from the MRI brain images:
And this is the corresponding WMH image from the WMH dataset:
How can I use the other images in network validation?
I suppose there will be a loss function and this network is supervised learning.