I am implementing a code for semantic segmentation using Keras and I wrote my loss function as in the paper "Generalised Dice overlap as a deep learning ...
26.02.2018 · I just implemented the generalised dice loss (multi-class version of dice loss) in keras, as described in ref: (my targets are defined as: (batch_size, image_dim1, image_dim2, image_dim3, nb_of_classes))
def generalized_dice_loss_w(y_true, y_pred): # Compute weights: "the ... Plus I believe it would be usefull to the keras community to have a generalised dice loss implementation, as it seems to be used in most of recent semantic segmentation tasks (at least in the medical image community).
02.07.2021 · Generalized Wasserstein Dice Loss. The Generalized Wasserstein Dice Loss (GWDL) is a loss function to train deep neural networks for applications in medical image multi-class segmentation.. The GWDL is a generalization of the Dice loss and the Generalized Dice loss that can tackle hierarchical classes and can take advantage of known relationships between …
Plus I believe it would be usefull to the keras community to have a generalised dice loss implementation, as it seems to be used in most of recent semantic segmentation tasks (at least in the medical image community). PS: it seems odd to me how the weights are defined; I get values around 10^-10.
dice_loss_for_keras.py. """. Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss. It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy. """. # define custom loss and metric functions. from keras import backend as K.
Dice Loss¶. The Dice coefficient, or Dice-Sørensen coefficient, is a common metric for pixel segmentation that can also be modified to act as a loss ...