  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
