Dice function for binary segmentation problems

Source: R/customMetrics.R

Note: Assumption is that y_true is not a one-hot representation of the segmentation batch. For use with e.g., sigmoid activation.

binary_dice_coefficient(y_true, y_pred, smoothingFactor = 0)

Arguments

y_true

True labels (Tensor)
y_pred

Predictions (Tensor of the same shape as y_true)
smoothingFactor

parameter for smoothing the metric.

Value

Dice value (negative)

Author

Tustison NJ

Examples


library( ANTsR )
library( ANTsRNet )
library( keras )

model <- createUnetModel2D( c( 64, 64, 1 ) )

#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist

dice_loss <- binary_dice_coefficient( smoothingFactor = 0.1 )

model %>% compile( loss = dice_loss,
  optimizer = optimizer_adam( lr = 0.0001 ) )

#> Error in compile(., loss = dice_loss, optimizer = optimizer_adam(lr = 1e-04)): object 'model' not found

rm(model); gc()

#> Warning: object 'model' not found
#>           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
#> Ncells 2488151 132.9    4570014 244.1         NA  4570014 244.1
#> Vcells 4379727  33.5   10146329  77.5      65536  6511633  49.7