\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{mwe} % to get dummy images
\jmlrvolume{-- Under Review}
\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026 submission}
\editors{Under Review for MIDL 2026}

\title[Short Title]{Fourier Profiles Predict Failure Modes in Chest X-ray Classification Models}
% \title[Short Title]{Spectral Blind Spots: Failure Mode Discovery in X-Ray Models using Fourier Profiles}

 % 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{$^{1,2}$}}
% \orcid{1111-2222-3333-4444} \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{Samuel Halimi}
\orcid{1111-2222-3333-4444} \Email{samuel@azmed.co}\\
\Name{Loïc Themyr} \Email{loic@azmed.co}\\
\Name{Arnaud Abreu} \Email{arnaud@azmed.co}\\
\addr 10 rue d'Uzès 75002 Paris
}

\begin{document}

\maketitle

\begin{abstract}
Deep learning models for chest X-ray anomaly detection remain vulnerable to subtle distributional shifts (e.g., acquisition technique, patient-related factors and preprocessing). Traditional error analysis often relies on semantic metadata or model embeddings, which can mask low-level signal variations that degrade performance. In this work, we propose a data-centric framework for automated failure mode discovery using spectral analysis. We project images into the frequency domain and extract a compact profile summarizing the distribution of signal energy across frequency bands. By performing unsupervised clustering on these spectral profiles, we demonstrate that model failures are not randomly distributed but are strongly concentrated within specific spectral clusters. This method effectively isolates "blind spots", enabling the prediction of model reliability and the discovery of performance-degrading data slices without requiring ground-truth failure annotations.
\end{abstract}

\begin{keywords}
Chest X-ray, Failure Mode Discovery, Spectral Analysis, Model Robustness, Unsupervised Clustering.
\end{keywords}

\section{Introduction}
% Fail to generalize (distribution shifts)
Deep learning has achieved remarkable success in the interpretation of chest X-rays (CXRs), where algorithms now frequently match or exceed radiologist performance in controlled settings \cite{demander à Pauline}. However, in the context of clinical deployment to unseen centers, models often fail to generalize to data that deviates statistically from their training distribution, resulting in significant and unanticipated performance degradation \cite{zech2018variable, yu2022external}. Ensuring the reliability of these systems requires not only high accuracy, but also the ability to anticipate and identify failure modes before they impact patient care.
% Anticipating -> Generalization from training strategy -> Nope, on le mettra dans la partie "Fourier" du related work.

% Identify failures by categorical attributes.

Several methods have been proposed to isolate subsets or \emph{slices} of validation data where models underperform.
The standard clinical approach slices performance metrics by available categorical attributes, such as patient demographics, acquisition parameters, or equipment manufacturers \cite{pooch2020can, seyyed2020chexclusion}.
Although these methods are very interpretable and well suited to spot biases and fairness gaps, they highly depend on the quality and availability of clinical metadata and fail to capture distribution shifts at signal-level.

% Identify failures in latent space.

To overcome the above limitations, recent approaches in \emph{slice discovery} successfully automated the detection of failure modes by clustering images in the model’s latent embedding space \cite{eyuboglu2022domino}.
Yet, clustering in the model's latent space is paradoxical for failure discovery: deep networks are explicitly trained to be invariant to non-semantic features.
Consequently, the embedding space often suppresses the very signal-level irregularities that cause the model to fail, rendering them invisible to latent-based slice discovery methods.

% Our method

In this work, we propose a \emph{data-centric} framework that prioritizes signal characteristics over semantic features or metadata labels.
We hypothesize that some systematic model failures correlate with specific profiles in the frequency domain.
Using the \emph{Fourier transform} of images, we compute a compact profile summarizing the distribution of signal energy across frequency bands.

Our experiments demonstrate that these spectral clusters act as powerful predictors of model reliability. We show that model failures are not uniformly distributed but are concentrated within specific spectral clusters—effectively revealing "blind spots" in the model’s generalization capability. This approach allows for the identification of performance-degrading data slices rooted in image physics, enabling predictive reliability estimation without the need for ground-truth failure annotations.

Our contributions are as follows:
\begin{itemize}
    \item We propose a novel, label-free pipeline for identifying systematic failure modes in chest X-ray models using compact spectral descriptors.
    \item We demonstrate that unsupervised clustering in the frequency domain captures latent distributional shifts (such as acquisition artifacts and noise patterns) that are masked by latent-space analysis and unrecorded in metadata.
    \item We provide empirical evidence that these spectral clusters can predict model degradation, offering a practical tool for data curation and reliability estimation in clinical environments.
\end{itemize}


% Template

This is where the content of your paper goes.  Some random
notes\footnote{Random footnote are discouraged}:
\begin{itemize}
\item You should use \LaTeX \cite{Lamport:Book:1989}.
\item JMLR/PMLR uses natbib for references. For simplicity, here, \verb|\cite|  defaults to
  parenthetical citations, i.e. \verb|\citep|. You can of course also
  use \verb|\citet| for textual citations.
\item Eprints such as arXiv papers can of course be cited \cite{Hinton:arXiv:2015:Distilling}. We recomend using a \verb|@misc| bibtex entry for these as shown in the sample bibliography.
\item You should follow the guidelines provided by the conference.
\item Read through the JMLR template documentation for specific \LaTeX
  usage questions.
\item Note that the JMLR template provides many handy functionalities
such as \verb|\figureref| to refer to a figure,
e.g. \figureref{fig:example},  \verb|\tableref| to refer to a table,
e.g. \tableref{tab:example} and \verb|\equationref| to refer to an equation,
e.g. \equationref{eq:example}.
\end{itemize}

\begin{table}[htbp]
 % The first argument is the label.
 % The caption goes in the second argument, and the table contents
 % go in the third argument.
\floatconts
  {tab:example}%
  {\caption{An Example Table}}%
  {\begin{tabular}{ll}
  \bfseries Dataset & \bfseries Result\\
  Data1 & 0.12345\\
  Data2 & 0.67890\\
  Data3 & 0.54321\\
  Data4 & 0.09876
  \end{tabular}}
\end{table}

\begin{figure}[htbp]
 % Caption and label go in the first argument and the figure contents
 % go in the second argument
\floatconts
  {fig:example}
  {\caption{Example Image}}
  {\includegraphics[width=0.5\linewidth]{example-image}}
\end{figure}

\begin{algorithm2e}
\caption{Computing Net Activation}
\label{alg:net}
 % older versions of algorithm2e have \dontprintsemicolon instead
 % of the following:
 %\DontPrintSemicolon
 % older versions of algorithm2e have \linesnumbered instead of the
 % following:
 %\LinesNumbered
\KwIn{$x_1, \ldots, x_n, w_1, \ldots, w_n$}
\KwOut{$y$, the net activation}
$y\leftarrow 0$\;
\For{$i\leftarrow 1$ \KwTo $n$}{
  $y \leftarrow y + w_i*x_i$\;
}
\end{algorithm2e}

\clearpage  % Acknowledgements, references, and appendix do not count toward the page limit (if any)
% Acknowledgments---Will not appear in anonymized version
\midlacknowledgments{We thank a bunch of people.}


\bibliography{midl-samplebibliography}


\appendix

\section{Proof of Theorem 1}

This is a boring technical proof of
\begin{equation}\label{eq:example}
\cos^2\theta + \sin^2\theta \equiv 1.
\end{equation}

\section{Proof of Theorem 2}

This is a complete version of a proof sketched in the main text.

\end{document}
