Total Dataset :- 100 (on case level)
Training :- 76 cases (18000 slices) Validation :- 19 cases (4000 slices) Test :- 5 cases (2000 slices)
I have a dataset that consists of approx. Eighteen thousand images, out of which approx. Fifteen thousand images are of the normal patient and around 3000 images of patients having some diseases. Now, for these 18000 images, I also have their segmentation mask. So, 15000 segmentations masks are empty, and 3000 have patches.
Should I also feed my model (deep learning, i.e., unet with resnet34 backbone) empty masks along with patches?
EDIT:- patches are very small. Currently, I'm getting 57% IoU on validation. When I tested on test cases, I'm getting over-segmenatation.
