\documentclass{midl} % Include author names
%\documentclass[anon]{midl} % Anonymized submission

% 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{mwe} % to get dummy images
\usepackage{booktabs}
\usepackage{threeparttable}
\usepackage[nolist,nohyperlinks]{acronym}
\usepackage{dcolumn} % dcolumn to line up decimals

%\jmlrvolume{-- Under Review}
\jmlryear{2021}
\jmlrworkshop{Full Paper -- MIDL 2021}
%\editors{Under Review for MIDL 2021}

\title[\acf{SRA}]{\acl{SRA}: Learning Generalized Features from Sparsely-Labeled Data Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Phenotyping}

% \acl{SRA}: Learning Generalized Features of Unsupervised Domain Adaptation for Tissue Type Classification in Colorectal Cancer

 % 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}

 % Two authors with the same address
 % \midlauthor{\Name{Author Name1} \Email{abc@sample.edu}\and
 %  \Name{Author Name2} \Email{xyz@sample.edu}\\
 %  \addr Address}

 % Three or more authors with the same address:
 % \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}\\
% \addr Address 2
% }

%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
%\midlauthor{\Name{Author Name1\midljointauthortext{Contributed equally}\nametag{$^{1,2}$}} \Email{abc@sample.edu}\\
%\addr $^{1}$ Address 1 \\
%\addr $^{2}$ Address 2 \AND
%\Name{Author Name2\midlotherjointauthor\nametag{$^{1}$}} %\Email{xyz@sample.edu}\\
%\Name{Author Name3\nametag{$^{2}$}} %\Email{alphabeta@example.edu}\\
%\Name{Author Name4\midljointauthortext{Contributed %equally}\nametag{$^{3}$}} \Email{uvw@foo.ac.uk}\\
%\addr $^{3}$ Address 3 \AND
%\Name{Author Name5\midlotherjointauthor\nametag{$^{4}$}} %\Email{fgh@bar.com}\\
%\addr $^{4}$ Address 4
%}


\midlauthor{\Name{Christian Abbet\nametag{$^{1,2}$}}
    \Email{christian.abbet@epfl.ch}\\
    \addr $^{1}$  Signal Processing Laboratory 5 (LTS5), EPFL, Lausanne, Switzerland \\
    \addr $^{2}$ Institute of Pathology, University of Bern, Switzerland \AND
    % Linda Studer
    \Name{Linda Studer\nametag{$^{2,3,4}$}}
    \Email{linda.studer@unifr.ch}\\
    \addr $^{3}$ Document, Image and Video Analysis (DIVA) Research Group, University of Fribourg, Switzerland \\
    \addr $^{4}$ iCoSyS, University of Applied Sciences and Arts Western Switzerland, Switzerland \AND
    % Andreas Fisher
    \Name{Andreas Fischer\midlotherjointauthor\nametag{$^{3,4}$}}
    \Email{andreas.fischer@hefr.ch }\\
    % Heather Dawson
    \Name{Heather Dawson\midlotherjointauthor\nametag{$^{2}$}}
    \Email{heather.dawson@pathology.unibe.ch}\\
    % Inti Zlobec
    \Name{Inti Zlobec\midlotherjointauthor\nametag{$^{2}$}}
    \Email{inti.zlobec@pathology.unibe.ch}\\
    % Behzad Bozorgtabar
    \Name{Behzad Bozorgtabar\nametag{$^{1,5}$}}
    \Email{behzad.bozorgtabar@epfl.ch}\\
    \addr $^{5}$ Center for Biomedical Imaging (CIBM), Switzerland \AND
    % Jean-Philippe Thiran
    \Name{Jean-Philippe Thiran\nametag{$^{1,5,6,7}$}}
    \Email{jean-philippe.thiran@epfl.ch}\\
    \addr $^{6}$ University of Lausanne (UNIL), Switzerland\\
    \addr $^{7}$ Radiology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Switzerland
}


% Command for authors
\definecolor{violet}{rgb}{0.890, 0.443, 0.765} 
\definecolor{orange}{rgb}{0.961, 0.702, 0.259} 
\newcommand{\christian}[1]{{\color{violet} #1}}
\newcommand{\linda}[1]{{\color{red} #1}}

% Acronyms 
\input{acronym}

\begin{document}

\maketitle

\begin{abstract}
% PROBLEMS
Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology.
Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue.
However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to variations in tissue stainings, types, and textures.
Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets.
% SOLUTION
In this work, we propose \ac{SRA} which takes advantage of self-supervised learning to perform domain adaptation and removes the necessity of a fully-labeled source dataset.
\ac{SRA} can effectively transfer the discriminative knowledge obtained from a few labeled source domain to a new target domain without requiring additional tissue annotations. 
Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. 
We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue types and further validate our approach on our in-house clinical cohort. 
\end{abstract}

\begin{keywords}
Computational pathology, self-supervised learning, few labeled data, unsupervised domain adaptation, colorectal cancer
\end{keywords}


\input{source/I_II_intro_related_work}
\input{source/III_methods}
\input{source/V_results}
\input{source/VI_conclusion}
% Acknowledgments---Will not appear in anonymized version
\input{source/ack}
\bibliography{abbet16}
\input{source/appendix}


\end{document}
