Multiview:
Similar to Jakobs segmentation method. 3 UNets, each trained with a different perspective. Little improvement on Hippocampus, no difference on BraTS, but worse performance on other datasets -> Max be due to anisotropy

Conclusion: Do not use this

Dice reweighting:
Oversampling foreground makes the dice believe the foreground is more frequent than it actually is. Therefore, it does not punish false positives hard enough. I therefore tried to balance the dice by doing the following:

1. collect class frequencies from dataset
2. collect class frequencies from generated batches (where patches were sampled more frequently with foreground)
3. compute corrective factors for the dice (basically reduce the influence of TP and FN in the loss function)

Result: As expected the dice now predicts less voxels as foreground, but the results are overall worse (look for reweight in description in leaderboard)

Conclusion: Do not use