A survey of loss functions for semantic segmentation

Understand

Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars.

• In the past five years, various papers came up with different objective loss functions used in different cases such as biased data, sparse segmentation, etc.
• In this paper, we have summarized some of the well-known loss functions widely used for Image Segmentation and listed out the cases where their usage can help in fast and better convergence of a model.
• Furthermore, we have also introduced a new log-cosh dice loss function and compared its performance on the NBFS skull-segmentation open-source data-set with widely used loss functions.