\documentclass{midl} % Include author names
\usepackage{hyperref}
\usepackage{url}

\usepackage{amsmath}
\usepackage{amscd}
%\usepackage{subcaption}
\usepackage{color, colortbl}
\usepackage{arydshln}
\usepackage{nicematrix,tikz}
\usepackage{algorithm}
% \usepackage{algorithmic}
\usepackage{algpseudocode}
\usepackage{booktabs}
\usepackage{multirow}
%\usepackage{subfigure}
\newcommand{\T}{{\scriptscriptstyle \top}}
\newcommand{\comment}[1]{}
% 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
\jmlrvolume{-- 164}
\jmlryear{2025}
\jmlrworkshop{Full Paper -- MIDL 2025}
\editors{Accepted for publication at MIDL 2025}

\title[Calibrated Confidence Intervals]{Clinical Measurements with Calibrated Instance-Dependent Confidence Intervals}

 % 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{Rotem Nizhar$^{1}$} \Email{rotemnizhar10@gmail.com} \\
\addr $^{1} $Computer Science Dprt. Tel-Aviv University 
\AND
\Name{Lior Frenkel$^{2}$} \Email{liorfrenkel1992@gmail.com} \\
\Name{Jacob Goldberger$^{2}$} \Email{jacob.goldberger@biu.ac.il} \\
\addr $^{2} $Engineering Faculty, Bar-Ilan University
}
\begin{document}

\maketitle

\begin{abstract}
Reporting meaningful confidence intervals for the predictions of a regression neural network is critical in medical imaging applications since clinical decisions rely on network predictions. We expect to obtain larger intervals for difficult examples and smaller ones for easier examples to predict. We demonstrate that training a Gaussian regression network, followed by a non-parametric conformal prediction technique to scale the estimated variance, is the most effective way to achieve a small confidence interval with a coverage guarantee. Through extensive experiments on various medical imaging datasets and network architectures, we show that this combined training and calibration procedure produces improved results compared to previous methods.
Our code is publicly available\footnote{\url{https://github.com/rotem1023/Calibrated-Instance-Dependent}}.
\end{abstract}

\begin{keywords}
confidence interval,  uncertainty calibration, conformal prediction
\end{keywords}


\input{note}
 \bibliography{midl25_164}


\end{document}
