\documentclass{midl} % Include author names

% The following packages will be automatically loaded:
% jmlr, amsmath, amssymb, natbib, graphicx, url, algorithm2e
% ifoddpage, relsize and probably more
% make sure they are installed with your latex distribution
\usepackage{multirow}
\usepackage{wrapfig}
% \usepackage{subcaption}

\newcommand{\myparagraph}[1]{
\noindent \textbf{#1} ---
}

\usepackage[table]{xcolor}
\definecolor{Yellow}{RGB}{220,204,118}
\newcommand{\our}{\cellcolor{Yellow!15}}

\newcommand{\ours}{CIA-MIL\xspace}

\newcommand{\sd}[1]{$_{\pm#1}$}
\usepackage{mwe} % to get dummy images


\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\jmlrvolume{-- 310}
\editors{Accepted for publication at MIDL 2026}

\title[\ours]{Counterfactual Intervention in Attention Multiple Instance Learning For Digital Pathology}


\midlauthor{\Name{Imane Chraki\nametag{$^{1,2}$}} 
% \orcid{1111-2222-3333-4444} 
\Email{imane.chraki@centralesupelec.fr}\\
\Name{Pierre Marza\nametag{$^{1,2}$}} \Email{pierre.marza@centralesupelec.fr}\\
\Name{Stergios Christodoulidis\nametag{$^{1,2}$}} \Email{stergios.christodoulidis@centralesupelec.fr}\\
\Name{Maria Vakalopoulou\nametag{$^{1,2}$}} \Email{maria.vakalopoulou@centralesupelec.fr}\\
\addr $^1$ MICS Laboratory, CentraleSupélec, Université Paris-Saclay \\
  $^2$ IHU PRISM, National Center for Precision Medicine in Oncology, Gustave Roussy \\
}

\begin{document}
\maketitle

\begin{abstract}

Attention-based Multiple Instance Learning (MIL) has become a prominent framework for analysing whole-slide images (WSI). These models have been shown to achieve good performance on classification tasks, while also offering an inherent proxy for interpretability through attention weights. In this work, we first question the validity of using attention for the interpretability of MIL models. Subsequently, we propose Counterfactual Intervention in Attention for MIL (\ours), a causal extension of attention-based MIL that explicitly measures and optimizes the contribution of attention to slide-level predictions.  Across four histopathology classification benchmarks (BRCA, NSCLC, LUAD, Camelyon16) and two feature encoders (Resnet50, UNI), we investigate how the interpretability of attention relates to the representation space, and the downstream performance. We then show that \ours achieves performance comparable to strong MIL baselines while providing a more causally meaningful attention vector for explaining the model's outcome. Qualitative perturbation experiments show that dropping the top-attended patches leads to a larger confidence degradation in \ours compared to baseline ABMIL, highlighting the potential of causal supervision for reliable and interpretable WSI-based prediction.

\end{abstract}

\begin{keywords}
Multiple Instance Learning, Attention, Interpretability, Whole Slide Images, Digital Pathology, Causal Intervention.
\end{keywords}

\input{introduction}
\input{related_work}
\input{methods}
\input{setup}
\input{results_v1}
\input{ablation}



\section{Conclusion}

In this work, we questioned the reliability of attention as an explainability proxy for attention MIL models, and presented \ours, a causal attention learning framework for MIL that explicitly enforces the causal contribution of attention to model prediction through counterfactual supervision. Through extensive experiments on tumor subtyping, mutation prediction, and metastasis detection benchmarks, we demonstrated that high predictive performance does not necessarily imply reliable interpretability via attention. \ours consistently improves attention faithfulness while maintaining competitive predictive performance across both out-of-domain and in-domain feature spaces. This study establishes counterfactual causal intervention supervision of attention as a promising mechanism to improve the reliability of attention in MIL, taking a step further towards deploying trustworthy and clinically actionable AI systems in digital pathology.



\clearpage  
\midlacknowledgments{ This work has benefited from state financial aid, managed by the Agence Nationale de Recherche under the investment program integrated into France 2030, project references ANR-21-RHUS-0003, ANR-21-CE45-0007, ANR-23-CE45-0029, ANR-23-IAHU-0002, and ANR-23-IACL-0003 – DATAIA CLUSTER (as part of IA CLUSTER program).  Experiments have been conducted using HPC resources from the Mésocentre computing center of CentraleSupélec and École Normale Supérieure Paris-Saclay, supported by CNRS and Région Île-de-France, and resources from GENCI–IDRIS (Grant 2024-AD011015828). The results shown in this paper are part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.}

\bibliography{midl26_310}

\appendix
\input{appendix}


\end{document}
