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The soft-Dice loss is a very popular loss for image semantic segmentation in the medical field, and is often combined with the cross-entropy loss. It has recently been shown that the gradient of the dice loss is a “negative” of the ground truth, and its supervision can be trivially mimicked by multiplying the predicted probabilities with a pre-computed “gradient-map” (Kervadec and de Bruijne, 2023). In this short paper, we study the properties of the dice loss, and two of its variants (Milletari et al., 2016a; Sudre et al., 2017b) when sub-patching is required, and no foreground is present. As theory and experiments show, this introduce divisions by zero which are difficult to handle gracefully while maintaining good performances. On the contrary, the mime loss of (Kervadec and de Bruijne, 2023) proved to be far more suited for sub-patching and handling of empty patches.
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