DICE loss
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- Created a year ago
- Comments:16 (10 by maintainers)
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Understanding Dice Loss for Crisp Boundary Detection
Dice loss originates from Sørensen–Dice coefficient, which is a statistic developed in 1940s to gauge the similarity between two samples [Wikipedia].
Read more >Dice Loss Explained | Papers With Code
Introduced by Sudre et al. in Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations.
Read more >Dice-coefficient loss function vs cross-entropy - Cross Validated
One compelling reason for using cross-entropy over dice-coefficient or the similar IoU metric is that the gradients are nicer.
Read more >The Difference Between Dice and Dice Loss - PYCAD
The dice loss in a special case of the dice coefficient but physically they are the same. Read this arrticle for more details....
Read more >Loss Function Library - Keras & PyTorch | Kaggle
Combo loss is a combination of Dice Loss and a modified Cross-Entropy function that, like Tversky loss, has additional constants which penalise either...
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Top GitHub Comments
So should I make a pr here
Interested