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Present segmentation practices utilize a differentiable surrogate metric, such as for example smooth Dice, as part of the reduction purpose throughout the learning period. In this work, we first shortly explain just how to derive volume estimates from a segmentation that is, potentially, naturally unsure or uncertain. This might be accompanied by a theoretical analysis and an experimental validation linking the inherent doubt to typical loss features for education CNNs, namely
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