\documentclass{midl}

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\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\jmlrvolume{-- nnn}
\editors{Accepted for publication at MIDL 2026}
\title[Generalizing Abstention]{Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation}


\midlauthor{\Name{Wesam Moustafa\nametag{$^{1,2}$}}\Email{wesam.h.moustafa@gmail.com}\\
\Name{Hossam Elsafty\nametag{$^{1,2,3}$}}\orcid{0009-0001-4095-4912}\Email{hossam.elsafty@iais.fraunhofer.de}\\
\Name{Helen Schneider\nametag{$^{1,2,3}$}}\Email{helen.schneider@iais.fraunhofer.de}\\
\Name{Lorenz Sparrenberg\nametag{$^{1,3}$}}\orcid{0000-0001-9450-7387}\Email{sparrenberg@bit.uni-bonn.de}\\
\Name{Rafet Sifa\nametag{$^{1,2,3}$}}\Email{rafet.sifa@iais.fraunhofer.de}\\
\addr $^{1}$ Institute of Computer Science, University of Bonn, Bonn, Germany \\
\addr $^{2}$ Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany \\
\addr $^{3}$ Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany
}

\begin{document}

\maketitle

\begin{abstract}
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 \url{https://github.com/wemous/abstention-for-segmentation}.
\end{abstract}

\begin{keywords}
Abstention, Medical Image Segmentation, Label Noise, Noise-Robust Learning, Loss Functions.
\end{keywords}

\input{1.intro}
\input{2.related}
\input{3.methodology}
\input{4.experiments}
\input{5.results}
\input{6.conclusions}

\clearpage 
\midlacknowledgments{This research has been funded by the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr-Institute for Machine Learning and Artificial Intelligence.}
\bibliography{midl26_91}
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\input{7.appendix}

\end{document}
