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What is BCE dice loss?
BCE-Dice Loss This loss combines Dice loss with the standard binary cross-entropy (BCE) loss that is generally the default for segmentation models. Combining the two methods allows for some diversity in the loss, while benefitting from the stability of BCE.
Why is Dice loss used?
The Dice coefficient is widely used metric in computer vision community to calculate the similarity between two images. Later in 2016, it has also been adapted as loss function known as Dice Loss [10].
Why do soft Dice lose?
Because our target mask is binary, we effectively zero-out any pixels from our prediction which are not “activated” in the target mask. This loss function is known as the soft Dice loss because we directly use the predicted probabilities instead of thresholding and converting them into a binary mask.
Which is better, Dice coefficient or cross entropy loss?
I would start with cross-entropy loss, which seems to be the standard loss for training segmentation networks, unless there was a really compelling reason to use Dice coefficient. As summarized by @shimao and @cherub, one cannot say apriori which one will work better on a particular dataset. The correct way is to try both and compare the results.
Why do people use Dice coefficient instead of IOU?
In general, it seems likely that training will become more unstable. The main reason that people try to use dice coefficient or IoU directly is that the actual goal is maximization of those metrics, and cross-entropy is just a proxy which is easier to maximize using backpropagation.
How to calculate the gradient of dice loss?
Dice coefficient double counts the intersection (TP). Dice coefficient is a measure of overlap between two masks.1 indicates a perfect overlap while 0 indicates no overlap. Dice Loss = 1 — Dice Coefficient. Easy! We calculate the gradient of Dice Loss in backpropagation.
How to write the Dice coefficient in differentiable form?
Meanwhile, if we try to write the dice coefficient in a differentiable form: 2 p t p 2 + t 2 or 2 p t p + t, then the resulting gradients wrt p are much uglier: 2 t ( t 2 − p 2) ( p 2 + t 2) 2 and 2 t 2 ( p + t) 2. It’s easy to imagine a case where both p and t are small, and the gradient blows up to some huge value.