\section{Discussion and Conclusion}
\label{sec:discussion}
Class imbalance-aware loss functions relevantly enhance classification performance, primarily by improving recognition of minority classes.
Notably, the most substantial enhancements are observed when combining F1 or MCC loss with standard cross-entropy loss.
In particular, this combination seems to stabilize training. 
This points to similarities between classification and segmentation methodologies, exemplified in frameworks like nnUNet~\cite{isensee2021nnu}, where the combination of Dice (which essentially is the F1 score) and cross-entropy is found to perform best, and underscores the effectiveness of such hybrid approaches.
Further, it's worth noting that when employing only class imbalance-aware loss functions, there can be instances where certain classes may be somewhat neglected, a scenario not encountered in the combined approach.
In summary, the demonstrated efficacy of class imbalance-aware loss functions, alongside their ease of implementation, computational efficiency, and adaptability across various medical imaging tasks, highlights their potential impact on advancing clinical applications and enhancing the accuracy and reliability of deep learning-based diagnostics in real-world healthcare settings. 
These properties call for future studies exploring more scenarios in medical image classification with class-imbalance-aware loss functions such as different network architectures or time-series data.
Ultimately, we show that integrating loss functions derived from popular metrics such as the F1 score and the MCC with standard cross-entropy loss results in more robust classifiers, with particular benefits for the minority class, thus underscoring its clinical relevance.