On recommendation of Kanak on stackoverflow I am posting this question here:
Currently I am experimenting with various loss functions and optimizers for my binary image segmentation problem. The loss functions that I use in my Unet however give different output segmentation maps.
I have a highly imbalanced dataset, thus I am trying dice loss for which the customized function is given below.
def dice_coef(y_true, y_pred, smooth=1): """ Dice = (2*|X & Y|)/ (|X|+ |Y|) = 2*sum(|A*B|)/(sum(A^2)+sum(B^2)) ref: https://arxiv.org/pdf/1606.04797v1.pdf """ intersection = K.sum(K.abs(y_true * y_pred), axis=-1) return (2. * intersection + smooth) / (K.sum(K.square(y_true), -1) + K.sum(K.square(y_pred), -1) + smooth) def dice_coef_loss(y_true, y_pred): return 1 - dice_coef(y_true, y_pred)
Binary cross entropy results in a probability output map, where each pixel has a color intensity that represents the chance of that pixel being the positive or negative class. However, when I use the dice loss function, the output is not a probability map but the pixels are classed as either 0 or 1.
My questions are:
1.How is it possible that these different loss functions have these vastly different results?
- Is there a way to customize the dice loss function so that the output segmentation map is a probability map similar to the one of binary crossentropy loss.