Hi,
I have a dataset with positive and negative samples for a segmentation task where:
1. “positive” means, that the image contains at least one object
2. “negative” means, that the image contains no object of interest.
For every positive sample, there are roughly 3 negative samples in the dataset.
Now when I evaluate my model on my validation set, the dice-score will be very high in the beginning as the model just predicts 0 everywhere. After some iterations, the dice score will start to increase again but fails to reach the high scores from the start of the training. This makes the automatic saving of the best performing model very hard, if not impossible.
Any suggestions how to balance the dice-score such that it doesn’t favor negative examples during validation?