
We show an example of all the image variations that we make part of the transformations. In Fig.~\ref{fig:example_variations_full_acdc} we show an example image from each transformed test set for ACDC and in Fig.~\ref{fig:example_variations_full_p158} we show an example image from each transformed test set of P158.

\begin{figure}[!htb]
    \centering
    \includegraphics[width=\linewidth]{figures/example_images/image_grid_acdc.pdf}
    \caption{Visualization of the 14 data variations, alongside the original image (top-left) for a test sample in the ACDC dataset. All transforms visualised at severity 3.}
    \label{fig:example_variations_full_acdc}
\end{figure}


\begin{figure}[!htb]
    \centering
    \includegraphics[width=\linewidth]{figures/example_images/image_grid_p158.pdf}
    \caption{Visualization of the 14 data variations, alongside the original image (top-left) for a test sample in the P158 dataset. All transforms visualised at severity 3.}
    \label{fig:example_variations_full_p158}
\end{figure}

The images show while the object of interest remains discernable to the human eye, the difference to the original sample is large. Some variations are not diagnostically relevant, like smoothing, severe random motion, but they serve as interesting examples of where human expertise might still outperform sophisticated deep learning methods, and through this study we explore how to bridge this gap to unknown variations without explicitly using them as augmentations.

