Hi @ptrblck:
Yes, one idea is making the 2D case a 3D one. I would have to then run inference across a stack of images for the patient, possibly shifting the model over one image at a time and averaging results. To do this, I think simply averaging the loss for each output mask would suffice?
I was also wondering if there was a good way to check consistency across the three image predictions as an input to the model. (I.e. the predictions should have a similar distribution and number of positive pixels should also be similar – the anatomy cannot shift rapidly across a single image).
Curious if you had any pointers or examples of people doing this in PyTorch!