# Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation

arXiv: https://arxiv.org/abs/2601.14039 · HTML: https://mlsanity.com/paper/2601.14039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https://mlsanity.com/paper/1704.06857/) (2017)
- [Combating Label Noise in Deep Learning Using Abstention](https://mlsanity.com/paper/1905.10964/) (2019)
- [Adaptive Early-Learning Correction for Segmentation from Noisy Annotations](https://mlsanity.com/paper/2110.03740/) (2021)
- [Disentangling Human Error from the Ground Truth in Segmentation of Medical Images](https://mlsanity.com/paper/2007.15963/) (2020)
- [Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation](https://mlsanity.com/paper/1907.11835/) (2019)
- [Learning to Segment from Noisy Annotations: A Spatial Correction Approach](https://mlsanity.com/paper/2308.02498/) (2023)
- [Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2103.14242/) (2021)
- [Robust T-Loss for Medical Image Segmentation](https://mlsanity.com/paper/2306.00753/) (2023)
- [Distilling effective supervision for robust medical image segmentation with noisy labels](https://mlsanity.com/paper/2106.11099/) (2021)
- [L2B: Learning to Bootstrap Robust Models for Combating Label Noise](https://mlsanity.com/paper/2202.04291/) (2022)
- [Mitigating Label Noise through Data Ambiguation](https://mlsanity.com/paper/2305.13764/) (2023)
- [CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation](https://mlsanity.com/paper/2405.01217/) (2024)
- [Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels](https://mlsanity.com/paper/2501.06678/) (2025)
- [Improve Noise Tolerance of Robust Loss via Noise-Awareness](https://mlsanity.com/paper/2301.07306/) (2023)
- [Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning](https://mlsanity.com/paper/2306.05497/) (2023)
- [Active Negative Loss: A Robust Framework for Learning with Noisy Labels](https://mlsanity.com/paper/2412.02373/) (2024)
- [Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans](https://mlsanity.com/paper/1909.08959/) (2019)
- [Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems](https://mlsanity.com/paper/2410.21014/) (2024)

## Abstract (arXiv)

Label noise is a critical problem in medical image segmentation, often arising from the inherent difficulty of manual annotation. Models trained on noisy data are prone to overfitting, which degrades their generalization performance. While a number of methods and strategies have been proposed to mitigate noisy labels in the segmentation domain, this area remains largely under-explored. The abstention mechanism has proven effective in classification tasks by enhancing the capabilities of Cross Entropy, yet its potential in segmentation remains unverified. In this paper, we address this gap by introducing a universal and modular abstention framework capable of enhancing the noise-robustness of a diverse range of loss functions. Our framework improves upon prior work with two key components: an informed regularization term to guide abstention behaviour, and a more flexible power-law-based auto-tuning algorithm for the abstention penalty. We demonstrate the framework's versatility by systematically integrating it with three distinct loss functions to create three novel, noise-robust variants: GAC, SAC, and ADS. Experiments on the CaDIS and DSAD medical datasets show our methods consistently and significantly outperform their non-abstaining baselines, especially under high noise levels. This work establishes that enabling models to selectively ignore corrupted samples is a powerful and generalizable strategy for building more reliable segmentation models. Our code is publicly available at https://github.com/wemous/abstention-for-segmentation.