Inspired by the focal loss [3] that down-weights the well-segmented classes, our proposed Focal Dice Loss (FDL) considers the imbalance among structures of ...
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Authors:Michael Yeung, Evis Sala, ...
20.09.2018 · Abstract. For accurate tumor segmentation in brain magnetic resonance (MR) images, the extreme class imbalance not only exists between the foreground and background, but among different sub-regions of tumor. Inspired by the focal loss [ 3] that down-weights the well-segmented classes, our proposed Focal Dice Loss (FDL) considers the imbalance ...
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.
This loss combines Dice loss with the standard binary cross-entropy (BCE) loss ... Their paper "Focal Loss for Dense Object Detection" is retrievable here: ...
The Unified Focal loss is a new compound loss function that unifies Dice-based and cross entropy-based loss functions into a single framework. By incorporating ...
24.11.2020 · In the paper the combo loss of focal loss and dice loss is calculated using the following equation: combo loss= β*focalloss - (log (dice loss)) Kindly report your results if you wish to use any other combination of these losses. Share. Improve this answer. Follow this answer to receive notifications. answered Jan 4 at 14:31.