论文标题
部分可观测时空混沌系统的无模型预测
Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
论文作者
论文摘要
骰子丢失被广泛用于医学图像分割，并且已经提出了许多基于此类损失的改进损失功能。但是，仍然可以进一步改善骰子损失。在这项研究中，我们重新考虑了使用骰子损失的使用，并发现可以通过简单的方程式转换使用余弦相似性在损失函数中重写骰子丢失。使用这些知识，我们基于T-VMF相似性而不是余弦相似性提出了一种新颖的T-VMF骰子损失。基于T-VMF的相似性，我们提出的骰子损失比原始骰子损失更紧凑。此外，我们提出了一种有效的算法，该算法会使用验证精度自动确定T-VMF相似性的参数$κ$，称为自适应T-VMF骰子损失。使用该算法，可以将更紧凑的相似性应用于易于类别的较大类别，并为困难的类别提供更广泛的相似性，并且我们能够根据类的准确性来实现自适应培训。通过使用五倍的交叉验证在四个数据集上进行的实验，我们确认与原始骰子损失和其他损失功能相比，骰子得分系数（DSC）得到了进一步改进。
Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter $κ$ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMf Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve an adaptive training based on the accuracy of the class. Through experiments conducted on four datasets using a five-fold cross validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.
