Polynomial Dice Loss for Medical Image Segmentation
2026-06-22 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address challenges in medical image segmentation, especially with imbalanced data and detecting small lesions. They introduce Polynomial Dice Loss, which builds on the popular Dice Loss by using a polynomial expression to better control how mistakes are counted. This new loss function allows users to adjust how much different types of errors matter during training. Their tests show that this approach works well compared to traditional methods across various segmentation tasks.
Medical image segmentationDice LossData imbalanceLesion detectionPolynomial lossTaylor expansionTversky coefficientLoss functionComputer-assisted intervention
Authors
Hiroaki Aizawa
Abstract
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.