论文标题
分段损失奥德赛
Segmentation Loss Odyssey
论文作者
论文摘要
损失功能是基于深度学习的医学图像分割方法中的关键成分之一。现有文献中已经提出了许多损失功能，但进行了分开研究或仅研究了其他损失。在本文中，我们提出了系统的分类法，将现有损失功能分为四个有意义的类别。这有助于揭示它们之间的联系和基本相似之处。此外，我们探讨了基于传统的区域与最新基于边界的损失功能之间的关系。这些损失功能的pytorch实现可在\ url {https://github.com/junma11/segloss}上公开获得。
Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods. Many loss functions have been proposed in existing literature, but are studied separately or only investigated with few other losses. In this paper, we present a systematic taxonomy to sort existing loss functions into four meaningful categories. This helps to reveal links and fundamental similarities between them. Moreover, we explore the relationship between the traditional region-based and the more recent boundary-based loss functions. The PyTorch implementations of these loss functions are publicly available at \url{https://github.com/JunMa11/SegLoss}.
