How to train a U-Net with negative examples?
I trained U-Net with pictures of hands and fingers. The ground truth data are binary masks with white pixels for the foreground object (finger/hand) and black pixels for the background object. Now I want to add negatives, i.e. images without hand/finger. The respective ground truth would then be completely black. However, the dice coefficient is not suitable as a metric or loss function. The reason for this is described here:
" If smooth is set too low, when the ground truth has few to 0 white pixels and the predicted image has some non-zero number of white pixels, the model will be penalized more heavily. Setting smooth higher means if the predicted image has some low amount of white pixels when the ground truth has none, the loss value will be lower. Depending on how aggressive the model needs to be, though, maybe a lower value is good..." Correct Implementation of Dice Loss in Tensorflow / Keras
My question now is, does anyone have any experience on how best to train a U-Net with negatives?
