Function (biology)

Biology

Materials science

Range (aeronautics)

Telecommunications

Set (abstract data type)

Computer science

Code (set theory)

Scale-space segmentation

Evolutionary biology

Image segmentation

Segmentation

Pattern recognition (psychology)

Data set

Ranging

Artificial intelligence

Composite material

Programming language

Computer vision

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. We also showcased that certain loss functions perform well across all data-sets and can be taken as a good baseline choice in unknown data distribution scenarios. Our code is available at Github: https://github.com/shruti-jadon/Semantic-Segmentation-Loss-Functions.

A survey of loss functions for semantic segmentation