This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter. This parameter is updated during the backpropagation process, eliminating the need for additional computation or prior information about the level and spread of noisy labels. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets for skin lesion and lung segmentation. We also demonstrate the ability of T-Loss to handle different types of simulated label noise, resembling human error. Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. The project website can be found at https://robust-tloss.github.io

本文提出一种新的鲁棒性Loss函数——T-Loss，该函数基于学生-t分布的负对数似然，可以通过单个参数有效地处理数据中的异常值，并且在反向传播过程中更新该参数，从而消除了需要进行额外计算或关于噪声标签的级别和分布的先前信息的需要。我们的实验表明，在皮肤病和肺分割的两个公共医学数据集上，T-Loss在Dice分数方面优于传统Loss函数。我们还展示了T-Loss处理不同类型的模拟标签噪声的能力，这些噪声类似于人为误差。我们的结果提供了有力的证据，表明T-Loss是一种有前途的选择，可以处理医学图像分割中典型的高噪声或异常值情况。

医学图像分割的鲁棒T损失函数