# Label noise in segmentation networks : mitigation must deal with bias

arXiv: https://arxiv.org/abs/2107.02189 · HTML: https://mlsanity.com/paper/2107.02189/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Learning From Noisy Singly-labeled Data](https://mlsanity.com/paper/1712.04577/) (2017)
- [Learning to Segment Skin Lesions from Noisy Annotations](https://mlsanity.com/paper/1906.03815/) (2019)
- [Deep semi-supervised segmentation with weight-averaged consistency targets](https://mlsanity.com/paper/1807.04657/) (2018)
- [A Survey of Crowdsourcing in Medical Image Analysis](https://mlsanity.com/paper/1902.09159/) (2019)
- [Regularized Minimax Conditional Entropy for Crowdsourcing](https://mlsanity.com/paper/1503.07240/) (2015)
- [Towards annotation-efficient segmentation via image-to-image translation](https://mlsanity.com/paper/1904.01636/) (2019)

## Built on by (most cited first, 10 of 10)

- [How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model](https://mlsanity.com/paper/2312.10600/) (2023)
- [Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization](https://mlsanity.com/paper/2504.02996/) (2025)
- [Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation](https://mlsanity.com/paper/2605.06891/) (2026)
- [From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments](https://mlsanity.com/paper/2605.29565/) (2026)
- [Noisy Image Segmentation With Soft-Dice](https://mlsanity.com/paper/2304.00801/) (2023)
- [MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries](https://mlsanity.com/paper/2311.10380/) (2023)
- [How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?](https://mlsanity.com/paper/2412.08081/) (2024)
- [Marginal Thresholding in Noisy Image Segmentation](https://mlsanity.com/paper/2304.04116/) (2023)
- [Multi-style conversion for semantic segmentation of lesions in fundus images by adversarial attacks](https://mlsanity.com/paper/2410.13822/) (2024)
- [Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets](https://mlsanity.com/paper/2505.20928/) (2025)

## Abstract (arXiv)

Imperfect labels limit the quality of predictions learned by deep neural networks. This is particularly relevant in medical image segmentation, where reference annotations are difficult to collect and vary significantly even across expert annotators. Prior work on mitigating label noise focused on simple models of mostly uniform noise. In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data. We found that supervised and semi-supervised segmentation methods are robust or fairly robust to unbiased errors but sensitive to biased errors. It is therefore important to identify the sorts of errors expected in medical image labels and especially mitigate the biased errors.