
%\subsection{Tackling imbalance in medical imaging}
%\begin{itemize}
%    \item Since medical imaging often deals with imbalanced and thus existing works already approached the problem for different subproblems and other imaging modalities.
%    \item small lesion detection \cite{briaAddressingClassImbalance2020}
%    \item One class classification \cite{gaoHandlingImbalancedMedical2020}
%    \item overfitting in segmentation in imbalanced class settings \cite{liAnalyzingOverfittingClass2021}
%    \item breast tissue classification from OCT images. they also use the soft MCC loss for multi class classification. However, not 3D, not MR images. \cite{fooMulticlassClassificationBreast2022}
%\end{itemize}

% \subsection{Automated glioma classification}
% \begin{itemize}
%     \item Glioma classification is an important task performed in clinic as a basis for treatment planning
%     \item With new WHO glioma specifications~\cite{wen2021WHOClassification2021,louis2016WorldHealth2016}, new challenges arise for doctors as well as researchers finding new models to represent the changes in the specification.
%     \item Often only binary classification is done, because of strong imbalance in classes and difficulty in discerning Astrocytoma and Oligodendroglioma \begin{itemize}
%         \item binary classification: \cite{cindil2022prediction, van2019predicting}
%         \item \cite{bhandariNoninvasiveDeterminationIDH2021}: radiomics based approach
%         \item \cite{foltyn-dumitruImpactSignalIntensity2023}: comparison on signal intensity normalization with poor results on the minority class, which is missing from their analysis.
%         \item \cite{cluceruImprovingNoninvasiveClassification2022}: rely on additional diffusion weighted imaging
%     \end{itemize} 
% \end{itemize}