research-article

A survey of loss functions for semantic segmentation

Author:

Shruti JadonAuthors Info & Claims

2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Pages 1 - 7

https://doi.org/10.1109/CIBCB48159.2020.9277638

Published: 27 October 2020 Publication History

Abstract

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.

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Yang CLi SFang JLiang RXie LZhang XShen WTian Q(2024)GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian SplattingACM Transactions on Graphics10.1145/368775943:6(1-13)Online publication date: 19-Dec-2024
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Index Terms

A survey of loss functions for semantic segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Oct 2020

252 pages

Copyright © 2020.

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IEEE Press

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Published: 27 October 2020

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Cited By

Yang CLi SFang JLiang RXie LZhang XShen WTian Q(2024)GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian SplattingACM Transactions on Graphics10.1145/368775943:6(1-13)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687759
Vasu DSong SKainz HLee J(2024)MRI Segmentation of Musculoskeletal Components Using U-Net: Preliminary ResultsProceedings of the 2024 14th International Conference on Bioscience, Biochemistry and Bioinformatics10.1145/3640900.3640902(30-35)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3640900.3640902
Liu XYu TXie KWu JLi SAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Interact with the Explanations: Causal Debiased Explainable Recommendation SystemProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635855(472-481)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635855
Manko MPopov AGorriz JRamirez J(2024)Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisationCAAI Transactions on Intelligence Technology10.1049/cit2.123039:4(850-865)Online publication date: 25-Mar-2024
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Bouanane KDokkar BAllaoui MMeddour BKherfi MHedjam R(2024)Behaviors of first-order optimizers in the context of sparse data and sparse modelsDigital Signal Processing10.1016/j.dsp.2024.104637153:COnline publication date: 1-Oct-2024
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Monnier QPouli TKpalma K(2024)Survey on fast dense video segmentation techniquesComputer Vision and Image Understanding10.1016/j.cviu.2024.103959241:COnline publication date: 1-Apr-2024
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Shukla SGupta AGupta P(2024)Exploring the feasibility of adversarial attacks on medical image segmentationMultimedia Tools and Applications10.1007/s11042-023-15575-883:4(11745-11768)Online publication date: 1-Jan-2024
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Zhao GYang YLiu JChen NShen YWen HLan GOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)EV-EyeProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668838(62169-62182)Online publication date: 10-Dec-2023
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