A survey of loss functions for semantic segmentation

By Shruti Jadon. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2020

1,069 citations recorded by the source corpus.

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 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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b8601c86905b0184b9387b042400609febb93d10

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In Computer Vision and Pattern Recognition

1. Evis Sala
2. Carola-Bibiane Schönlieb
3. Michael Yeung
4. Leonardo Rundo

Citations in the corpus

• Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021, methodology): “Similarly, a comparison of fifteen loss functions using the NBFS Skull-stripped dataset (Jadon, 2020) (brain CT segmentation), which also introduces the log-cosh Dice loss, concluded that Focal Tversky loss and Tversky loss, both region-based losses, are generally optimal (Jadon, 2020).”