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github.com/Nacriema/Loss-Functions-For-Semantic-Segmentation
/ types & classes
Types & classes
10 in github.com/Nacriema/Loss-Functions-For-Semantic-Segmentation
⨍
Functions
23
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Types & classes
10
↓ 1 callers
Class
ComboLoss
It is defined as a weighted sum of Dice loss and a modified cross entropy. It attempts to leverage the flexibility of Dice loss of class imb
loss/__init__.py:339
Class
BatchSoftDice
loss/__init__.py:120
Class
CompoundedLoss
loss/__init__.py:329
Class
ExponentialLogarithmicLoss
This loss is focuses on less accurately predicted structures using the combination of Dice Loss and Cross Entropy Loss Original pape
loss/__init__.py:386
Class
FocalLoss
loss/__init__.py:155
Class
FocalTverskyLoss
More information about this loss, see: https://arxiv.org/pdf/1810.07842.pdf This loss is similar to Tversky Loss, but with a small adjustment
loss/__init__.py:216
Class
LogCoshDiceLoss
L_{lc-dce} = log(cosh(DiceLoss)
loss/__init__.py:245
Class
SensitivitySpecificityLoss
loss/__init__.py:310
Class
SoftDiceLoss
loss/__init__.py:73
Class
TverskyLoss
Tversky Loss is the generalization of Dice Loss It in the group of Region-Base Loss
loss/__init__.py:184