\documentclass{midl} % Include author names

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\graphicspath{{figures/}}

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\usepackage{mwe} % to get dummy images

% \jmlrvolume{-- Under Review}
% \jmlryear{2025}
% \jmlrworkshop{Full Paper -- MIDL 2025 submission}
% \editors{Under Review for MIDL 2025}

\jmlryear{2025}
\jmlrworkshop{Full Paper -- MIDL 2025}
\jmlrvolume{-- nnn}
\editors{Accepted for publication at MIDL 2025}

\title{Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology}

 % Use \Name{Author Name} to specify the name.
 % If the surname contains spaces, enclose the surname
 % in braces, e.g. \Name{John {Smith Jones}} similarly
 % if the name has a "von" part, e.g \Name{Jane {de Winter}}.
 % If the first letter in the forenames is a diacritic
 % enclose the diacritic in braces, e.g. \Name{{\'E}louise Smith}

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 % \midlauthor{\Name{Author Name1} \Email{abc@sample.edu}\and
 %  \Name{Author Name2} \Email{xyz@sample.edu}\\
 %  \addr Address}

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 % \midlauthor{\Name{Author Name1} \Email{an1@sample.edu}\\
 %  \Name{Author Name2} \Email{an2@sample.edu}\\
 %  \Name{Author Name3} \Email{an3@sample.edu}\\
 %  \addr Address}


% Authors with different addresses:
% \midlauthor{\Name{Author Name1} \Email{abc@sample.edu}\\
% \addr Address 1
% \AND
% \Name{Author Name2} \Email{xyz@sample.edu}\\
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% }

%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
\midlauthor{\Name{Fuyao Chen\nametag{$^{1,4}$}} 
% \orcid{1111-2222-3333-4444} 
\Email{fuyao.chen@yale.edu}\\
% \addr $^{2}$ Address 2 \AND
\Name{Yuexi Du\nametag{$^{1}$}} \Email{yuexi.du@yale.edu}\\
\Name{Tal Zeevi\nametag{$^{1}$}} \Email{tal.zeevi@yale.edu}\\
\Name{Nicha C. Dvornek\nametag{$^{1,2}$}} \Email{nicha.dvornek@yale.edu}\\
\Name{John A. Onofrey\nametag{$^{1,2,3}$}} \Email{john.onofrey@yale.edu}\\
\addr $^{1}$ Department of Biomedical Engineering, Yale University, New Haven, CT \\
\addr $^{2}$ Department of Radiology \& Biomedical Imaging, Yale University, New Haven, CT \\
\addr $^{3}$ Department of Urology, Yale University, New Haven, CT \\
\addr $^{4}$ Medical Scientist Training Program, Yale University, New Haven, CT \\
}

\begin{document}

\maketitle

\begin{abstract}
Histopathology evaluation of tissue specimens through microscopic examination is essential for accurate disease diagnosis and prognosis. However, traditional manual analysis by specially trained pathologists is time-consuming, labor-intensive, cost-inefficient, and prone to inter-rater variability, potentially affecting diagnostic consistency and accuracy. As digital pathology images continue to proliferate, there is a pressing need for automated analysis to address these challenges. Recent advancements in artificial intelligence-based tools such as machine learning (ML) models, have significantly enhanced the precision and efficiency of analyzing histopathological slides. However, despite their impressive performance, ML models are invariant only to translation, lacking invariance to rotation and reflection. This limitation restricts their ability to generalize effectively, particularly in histopathology, where images intrinsically lack meaningful orientation. In this study, we develop robust, equivariant histopathological biomarkers through a novel symmetric convolutional kernel via unsupervised segmentation. The approach is validated using prostate tissue micro-array (TMA) images from 50 patients in the Gleason 2019 Challenge public dataset. The biomarkers extracted through this approach demonstrate enhanced robustness and generalizability against rotation compared to models using standard convolution kernels, holding promise for enhancing the accuracy, consistency, and robustness of ML models in digital pathology. Ultimately, this work aims to improve diagnostic and prognostic capabilities of histopathology beyond prostate cancer through equivariant imaging. The code is available at \url{https://github.com/fyc423/SRE_Unsupervised_Segm}
\end{abstract}

\begin{keywords}
Histopathology, Unsupervised Segmentation, Equivariance, Convolutional Neural Network, Prostate cancer
\end{keywords}


\input{Introduction}
\input{Background}
\input{Methods}
\input{Results}
\input{DiscussionConclusion}




\section*{Acknowledgments}
F.C. was supported by the National Institute of Health (NIH) Medical Scientist Training Program Training Grant T32GM007205.

\bibliography{midl25_122}


\input{Appendix}


\end{document}



    
            
            

