改进医学图像分割的损失函数，提升小病灶检测效果。
Polynomial Dice Loss for Medical Image Segmentation
- 用泰勒展开将Dice损失转为多项式形式，可调节高阶项贡献
- 在多个数据集上优于传统Dice和Tversky损失，小病灶分割更准
- 适合处理数据不平衡的医学图像分割任务
医学图像分割是医学图像处理与计算机辅助干预的基础任务，但数据不平衡和小病灶检测仍是重大挑战。Dice Loss通过度量预测区域与真实区域的重叠，被广泛用于缓解这些问题。本文提出Polynomial Dice Loss，作为Dice Loss的多项式扩展。具体而言，利用Dice Loss的几何特性，通过泰勒展开将其表示为多项式形式，从而可调节损失函数中高阶成分的贡献。实验中，我们将该方法与基于传统Dice和Tversky系数的损失函数进行对比。结果表明，多项式形式提供了一种简便方式来调控损失形状，并在多个分割场景下实现了具有竞争力的性能。
原文摘要 · Abstract (English)
Medical image segmentation is a fundamental task for medical image processing and computer-assisted intervention, yet data imbalance and small lesion detection pose significant challenges. Dice Loss, which measures the overlap between predicted and ground truth regions, is widely used to mitigate these issues. To further emphasize its properties, we propose Polynomial Dice Loss, a polynomial extension of Dice Loss. Specifically, by leveraging the geometric characteristics of Dice Loss and formulating the loss function as a polynomial representation via Taylor expansion, we enable the adjustment of the contribution of higher-order components to the loss function. In our experiments, we evaluate the proposed method against loss functions derived from conventional Dice and Tversky coefficients. Experimental results and further analysis show that the polynomial formulation provides a simple way to control the loss shape and achieves competitive performance across multiple segmentation settings.
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