提出新损失函数HyTver，提升多发性硬化病灶分割精度与稳定性。
HyTver: A Novel Loss Function for Longitudinal Multiple Sclerosis Lesion Segmentation
- 融合Dice与距离度量，设计新型混合损失函数HyTver。
- Dice分数达0.659，同时保持距离指标性能良好。
- 适用于预训练模型，对复杂数据不平衡问题有效。
纵向多发性硬化病灶分割面临输入与输出数据不平衡的挑战，因此需设计更优的损失函数以提升模型实用性。现有方法多采用简单的Dice损失或交叉熵损失及其组合，缺乏针对性。尽管已有多种损失函数声称可缓解不平衡问题，但存在计算复杂、超参数过多（如指数项）或在非区域指标上表现不佳等缺陷。本文提出一种新型混合损失函数HyTver，兼顾分割性能与其他指标表现。实验表明，该方法在测试集上取得0.659的Dice分数，且距离相关指标优于或接近主流损失函数。进一步评估了其在预训练模型上的稳定性，并与多种主流损失函数进行了全面比较，验证了其有效性与鲁棒性。
原文摘要 · Abstract (English)
Longitudinal Multiple Sclerosis Lesion Segmentation is a particularly challenging problem that involves both input and output imbalance in the data and segmentation. Therefore in order to develop models that are practical, one of the solutions is to develop better loss functions. Most models naively use either Dice loss or Cross-Entropy loss or their combination without too much consideration. However, one must select an appropriate loss function as the imbalance can be mitigated by selecting a proper loss function. In order to solve the imbalance problem, multiple loss functions were proposed that claimed to solve it. They come with problems of their own which include being too computationally complex due to hyperparameters as exponents or having detrimental performance in metrics other than region-based ones. We propose a novel hybrid loss called HyTver that achieves good segmentation performance while maintaining performance in other metrics. We achieve a Dice score of 0.659 while also ensuring that the distance-based metrics are comparable to other popular functions. In addition, we also evaluate the stability of the loss functions when used on a pre- trained model and perform extensive comparisons with other popular loss functions
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