\documentclass{midl} % Include author names

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\usepackage{mwe} % to get dummy images
\usepackage[table]{xcolor}
\usepackage{multirow}
\jmlrvolume{-- 285}
\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\editors{Accepted for publication at MIDL 2026}

\title[Evidential DualU-Net]{Evidential DualU-Net: Single-Pass Uncertainty for Cell Instance Segmentation}% — A First Step Toward Full EDL}

 % Three or more authors with the same address:
\midlauthor{\Name{David Anglada-Rotger} \orcid{0009-0009-9792-5889}\Email{david.anglada@upc.edu}\\
  \Name{Ferran Marques} \Email{ferran.marques@upc.edu}\\
  \Name{Montse Pardàs} \Email{montse.pardas@upc.edu}\\
  \addr Image Processing Group (GPI), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain}

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%\footnotetext[1]{Contributed equally}

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\begin{document}

\maketitle

\begin{abstract}
% Accurate and trustworthy cell instance segmentation requires models that not only detect and classify nuclei but also quantify how much evidence supports each prediction. We introduce Evidential DualU-Net, the first evidential framework for multi-task cell instance segmentation. Its segmentation head outputs Dirichlet concentration parameters, enabling single-pass, closed-form aleatoric, epistemic, and vacuity uncertainties at both pixel and instance level, while its centroid decoder is equipped with two lightweight geometric uncertainty cues that capture localisation reliability without auxiliary models or sampling. These evidential and geometric measures expose complementary failure modes and allow principled filtering of low-confidence nuclei. Across multi-tissue and multi-stain datasets, Evidential DualU-Net matches or surpasses deep ensembles in error separation while being substantially more efficient, maintains or improves calibration over deterministic baselines, and generalises across datasets without retuning. This work provides an interpretable and computationally practical uncertainty formulation for digital pathology. Code and weights are available at: \url{https://github.com/davidanglada/Evidential-DualU-Net}.

% Accurate and trustworthy cell instance segmentation requires models that not only detect and classify nuclei but also communicate how much evidence supports each prediction. DualU-Net is a fast and effective two-head multi-task architecture for this problem, but—like most deterministic models—it provides no principled uncertainty estimates. We introduce \emph{Evidential DualU-Net}, the first evidential framework for multi-task cell instance segmentation. Its segmentation head predicts Dirichlet concentration parameters, enabling single-pass, closed-form aleatoric, epistemic, and vacuity uncertainties at both pixel and instance level, while its centroid decoder is complemented with two lightweight geometric uncertainty cues that quantify localisation reliability without auxiliary models or sampling. Together, these evidential and geometric measures expose complementary failure modes and allow principled filtering of low-confidence nuclei. Across multi-tissue and multi-stain datasets, Evidential DualU-Net matches or surpasses deep ensembles in error separation at a fraction of the cost, maintains or improves calibration over deterministic baselines, and generalises across datasets without retuning. 

Accurate and trustworthy cell instance segmentation requires models that not only detect and classify nuclei but also communicate how much evidence supports each prediction. DualU-Net is a fast and effective two-head multi-task architecture for this problem, but—like most deterministic models—it provides no principled uncertainty estimates. We introduce \emph{Evidential DualU-Net}, the first evidential framework for multi-task cell instance segmentation.Its segmentation head predicts Dirichlet concentration parameters, enabling single-pass, closed-form aleatoric, epistemic, and vacuity uncertainties at the pixel level, with instance-level quantities obtained via size-invariant pooling of pixel evidence. The centroid decoder is complemented with two lightweight geometric uncertainty cues that quantify localisation reliability without auxiliary models or sampling. Together, these evidential and geometric measures expose complementary failure modes and allow principled filtering of low-confidence nuclei. Across multi-tissue and multi-stain datasets, Evidential DualU-Net matches or surpasses deep ensembles and MC Dropout in error separation at a fraction of the cost, maintains or improves calibration over deterministic baselines, and generalises across datasets without retuning. This work provides an interpretable and computationally practical uncertainty formulation for digital pathology. Code and weights are available at: \url{https://github.com/davidanglada/Evidential-DualU-Net}.


\end{abstract}

\begin{keywords}
Cell Instance Segmentation, Evidential Deep Learning, Uncertainty Estimation, Multitask Learning, Nuclei Segmentation
\end{keywords}

\input{Sections/1.intro}
\input{Sections/2.sota}
\input{Sections/3.methods}
\input{Sections/4.results}
\input{Sections/5.conclusions}


\clearpage  % Acknowledgements, references, and appendix do not count toward the page limit (if any)
% Acknowledgments---Will not appear in anonymized version
\midlacknowledgments{This publication is part of the R\&D\&I project PID2023-148614OB-I00, funded by MICIU/AEI/10.13039/501100011033/ and by FEDER, EU. This research has also been funded by European Development Funds Regional, Programa operatiu FEDER de Catalunya 2014-2020 through the project DigiPatICS.}


\bibliography{midl26_285}


\appendix

\input{Sections/A.formulas_epi_ale}
\newpage
\input{Sections/B.formulas_limits}
\newpage
\input{Sections/AA.aggregation_ablation}
\newpage
\input{Sections/C.segmentation_histo}
\newpage
\input{Sections/D.centroid_histo}


\end{document}
