The soft Dice loss (SDL) has taken a pivotal role in many automated
segmentation pipelines in the medical imaging community. Over the last years,
some reasons behind its superior functioning have been uncovered and further
optimizations have been explored. However, there is currently no implementation
that supports its direct use in settings with soft labels. Hence, a synergy
between the use of SDL and research leveraging the use of soft labels, also in
the context of model calibration, is still missing. In this work, we introduce
Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a
standard setting with hard labels, but (ii) can be used in settings with soft
labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm
the potential synergy of DMLs with soft labels (e.g. averaging, label
smoothing, and knowledge distillation) over hard labels (e.g. majority voting
and random selection). As a result, we obtain superior Dice scores and model
calibration, which supports the wider adoption of DMLs in practice. Code is
available at
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本文介绍了 Dice 半度量损失（DMLs），该方法与标准设置下的软 Dice Loss（SDL）相同，在软标签设置下更优。实验结果表明，使用 DMLs 与软标签相结合可获得更好的分割结果以及模型校准，有助于其在实践中的广泛应用。