\section{Related Work}
\subsection{Overcoming class imbalance}
Considering how relevant a challenge class imbalance is for training clinically applicable deep learning models, several strategies have been developed to overcome this challenge. 
These can be broadly grouped into (i) sampling-based, (ii) loss-based, and (iii) synthesis-based approaches.

\emph{Sampling-based approaches} typically aim to oversample the minority class(es) or adjust loss weights. 
While easy to implement and computationally cheap, this can lead to the model severely overfitting the few samples available in the minority classes~\cite{zhengOversamplingMethodImbalanced2015}.

Alternatively, specific \emph{loss functions} such as focal loss~\cite{linFocalLossDense2017} may be used. 
Focal loss over-proportionally decreases the loss of an easy sample compared to difficult ones.
Since the samples in the minority class are potentially more challenging to classify due to their low prevalence, the model is penalized strongly for misclassifying them.
However, the focal formulation does not directly address the class imbalance but rather its "side effect" that minority samples are typically more challenging to classify.

Another strategy to tackle class imbalance is creating \textit{synthetic examples}. 
Earlier examples of such techniques include the Synthetic Minority Over-sampling Technique (SMOTE) \cite{chawlaSMOTESyntheticMinority2002}. 
In SMOTE, the minority class is over-sampled by taking each  sample and introducing synthetic examples along the line segments joining any/all of the $k$ minority class nearest neighbors.
Linear interpolation in image space, however, rarely gives sensible synthetic samples.
Hence, generative models, such as generative adversarial networks (GANs)~\cite{goodfellowGenerativeAdversarialNets2014a,qasimRedGANAttackingClass2020,liEIDGANGenerativeAdversarial2023} or diffusion models~\cite{qinClassBalancingDiffusionModels2023,dhariwalDiffusionModelsBeat2021a} have been developed to create realistic examples to supplement the minority classes.
While these are powerful approaches, they are also computationally expensive and require considerable effort to develop generative models that produce realistic, helpful minority class examples, which is again challenging.
Extending them to different tasks and classes usually requires extensive re-training.
