Hello PyTorch Community,
I am seeking some help/ideas on some training I want to pursue.
The idea is to train a VNET for a segmentation task and use its output for two tasks:
a) calculating the main loss (which could be DICE or whatever)
b) use it as the input to a pretrained AE model and calculating an additional loss
loss = mainLoss + additional loss
Pretrained model: AutoEncoder that was trained to learn the most prominent properties of the structure to segment.
Model to train: VNET
The idea is to have a pretrained model on a segmentation via autoencoder to preserve information about the shape of the structure to segment.
Do you have any tips on what to be aware of when doing that? Let me know if you have any questions.
Thank you,
Christian