The soft Dice loss (SDL) has taken a pivotal role in numerous 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 utilization in scenarios involving 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 employed 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. The code is available at https://github.com/zifuwanggg/JDTLosses

翻译：软Dice损失（SDL）在医学影像领域的众多自动分割流程中发挥着关键作用。近年来，其优越性能背后的部分原因已被揭示，并探索了进一步的优化方法。然而，目前尚无支持其在涉及软标签场景中直接应用的实现方案。因此，SDL的使用与软标签（包括模型校准场景）研究之间的协同效应仍未被充分开发。本文提出了Dice半度量损失（DMLs），其具有以下特性：(i) 在硬标签的标准设定下，其设计本质与SDL完全一致；(ii) 可适用于软标签设定。我们在公开的QUBIQ、LiTS和KiTS基准上的实验证实，相较于硬标签（如多数投票和随机选择），DMLs与软标签（如平均化、标签平滑和知识蒸馏）存在潜在协同优势。最终，我们获得了更优的Dice分数和模型校准性能，这支持了DMLs在实践中的更广泛应用。代码已开源至https://github.com/zifuwanggg/JDTLosses