Polynomial Dice Loss for Medical Image Segmentation
For medical image segmentation practitioners, this work offers a simple way to adjust loss shape to improve segmentation, though it is an incremental improvement over existing Dice Loss.
The paper proposes Polynomial Dice Loss, a polynomial extension of Dice Loss via Taylor expansion, to address data imbalance and small lesion detection in medical image segmentation. The method achieves competitive performance across multiple segmentation settings.
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.