shruti-jadon / Semantic-Segmentation-Loss-Functions

This Repository is implementation of majority of Semantic Segmentation Loss Functions

Semantic-Segmentation-Loss-Functions (SemSegLoss)

This Repository contains implementation of majority of Semantic Segmentation Loss Functions in Keras. Our paper is available open-source on following sites:

• Survey Paper DOI: 10.1109/CIBCB48159.2020.9277638
• Software Release DOI: https://doi.org/10.1016/j.simpa.2021.100078

In this paper we have summarized 15 such segmentation based loss functions that has been proven to provide state of the art results in different domain datasets.

Recently new los functions have also been added and we are still in process of adding more loss functions, so far we this repo consists of:

1. Binary Cross Entropy
2. Weighted Cross Entropy
3. Balanced Cross Entropy
4. Dice Loss
5. Focal loss
6. Tversky loss
7. Focal Tversky loss
8. log-cosh dice loss (ours)
9. Jaccard/IoU loss
10. SSIM loss
11. Unet3+ loss
12. BASNet loss

This paper is extension of our work on traumatic brain lesion segmentation published at SPIE Medical Imaging'20.

Github Code: https://github.com/shruti-jadon/Traumatic-Brain-Lesions-Segmentation

Citation

If you find our code useful, please consider citing our work using the bibtex:

@inproceedings{jadon2020survey,
  title={A survey of loss functions for semantic segmentation},
  author={Jadon, Shruti},
  booktitle={2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)},
  pages={1--7},
  year={2020},
  organization={IEEE}
}
@article{JADON2021100078,
title = {SemSegLoss: A python package of loss functions for semantic segmentation},
journal = {Software Impacts},
volume = {9},
pages = {100078},
year = {2021},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2021.100078},
url = {https://www.sciencedirect.com/science/article/pii/S2665963821000269},
author = {Shruti Jadon},
keywords = {Deep Learning, Image segmentation, Medical imaging, Loss functions},
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 recent years, various research papers proposed different loss functions used in case of biased data, sparse segmentation, and unbalanced dataset. In this paper, we introduce SemSegLoss, a python package consisting of some of the well-known loss functions widely used for image segmentation. It is developed with the intent to help researchers in the development of novel loss functions and perform an extensive set of experiments on model architectures for various applications. The ease-of-use and flexibility of the presented package have allowed reducing the development time and increased evaluation strategies of machine learning models for semantic segmentation. Furthermore, different applications that use image segmentation can use SemSegLoss because of the generality of its functions. This wide range of applications will lead to the development and growth of AI across all industries.}
}

Summarized Loss functions and their use-cases

#	Loss Function	Use cases
1	Binary Cross-Entropy	Works best in equal data distribution among classes scenarios Bernoulli distribution based loss function
2	Loss Function	Widely used with skewed dataset Weighs positive examples by Beta coefficient
3	Binary Cross-Entropy	Similar to weighted-cross entropy, used widely with skewed dataset weighs both positive as well as negative examples by Beta and 1 - Beta respectively
4	Weighted Cross-Entropy	Works best with highly-imbalanced dataset down-weight the contribution of easy examples, enabling model to learn hard examples
5	Balanced Cross-Entropy	Variant of Cross-Entropy Used for hard-to-segment boundaries
6	Focal Loss	Inspired from Dice Coefficient, a metric to evaluate segmentation results. As Dice Coefficient is non-convex in nature, it has been modified to make it more tractable.
7	Distance map derived loss penalty term	Inspired from Sensitivity and Specificity metrics Used for cases where there is more focus on True Positives.
8	Dice Loss	Variant of Dice Coefficient Add weight to False positives and False negatives.
9	Sensitivity-Specificity Loss	Variant of Tversky loss with focus on hard examples
10	Tversky Loss	Variant of Dice Loss and inspired regression log-cosh approach for smoothing Variations can be used for skewed dataset
11	Focal Tversky Loss	Inspired by Hausdorff Distance metric used for evaluation of segmentation Loss tackle the non-convex nature of Distance metric by adding some variations
12	Log-Cosh Dice Loss(ours)	Variant of Dice Loss and inspired regression log-cosh approach for smoothing Variations can be used for skewed dataset
13	Hausdorff Distance loss	Inspired by Hausdorff Distance metric used for evaluation of segmentation Loss tackle the non-convex nature of Distance metric by adding some variations
14	Shape aware loss	Variation of cross-entropy loss by adding a shape based coefficient used in cases of hard-to-segment boundaries.
15	Combo Loss	Combination of Dice Loss and Binary Cross-Entropy used for lightly class imbalanced by leveraging benefits of BCE and Dice Loss
16	Exponential Logarithmic Loss	Combined function of Dice Loss and Binary Cross-Entropy Focuses on less accurately predicted cases
18	Correlation Maximized Structural Similarity Loss	Focuses on Segmentation Structure. Used in cases of structural importance such as medical images.
19	Jaccard/IoU loss	Works well on balanced data Emphasizes more on large foreground regions
20	SSIM loss	Captures the structural information in an image. Focuses on only boundaries of an object