# SemSegLoss: A python package of loss functions for semantic segmentation

arXiv: https://arxiv.org/abs/2106.05844 · HTML: https://mlsanity.com/paper/2106.05844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 18 of 18)

- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation](https://mlsanity.com/paper/1805.02798/) (2018)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes](https://mlsanity.com/paper/1809.00076/) (2018)
- [Distance Map Loss Penalty Term for Semantic Segmentation](https://mlsanity.com/paper/1908.03679/) (2019)
- [SoftSeg: Advantages of soft versus binary training for image segmentation](https://mlsanity.com/paper/2011.09041/) (2020)
- [Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection](https://mlsanity.com/paper/1803.11078/) (2018)
- [Tilted Cross Entropy (TCE): Promoting Fairness in Semantic Segmentation](https://mlsanity.com/paper/2103.14051/) (2021)
- [A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery](https://mlsanity.com/paper/2104.01263/) (2021)
- [Correlation Maximized Structural Similarity Loss for Semantic Segmentation](https://mlsanity.com/paper/1910.08711/) (2019)
- [Boundary-aware Instance Segmentation](https://mlsanity.com/paper/1612.03129/) (2016)
- [A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial](https://mlsanity.com/paper/2006.01263/) (2020)
- [Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos](https://mlsanity.com/paper/2102.12173/) (2021)

## Built on by (most cited first, 3 of 3)

- [RankSEG: A Consistent Ranking-based Framework for Segmentation](https://mlsanity.com/paper/2206.13086/) (2022)
- [Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images](https://mlsanity.com/paper/2306.12510/) (2023)
- [Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?](https://mlsanity.com/paper/2301.04032/) (2023)

## Abstract (arXiv)

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.