\begin{table}[H]
\centering
\caption{Model performance comparison on the RADBOUD \cite{vandenHeuvel2018} fetal head segmentation task.}
\label{tab:radboud_comparison}
\resizebox{0.7\textwidth}{!}{%
\begin{tabular}{lccc}
\toprule
\textbf{Model}  & \textbf{Dice} & \textbf{IoU}  & \textbf{Recall}\\
\midrule
U-Net \cite{Ronneberger.2015}           & 0.928$\pm$0.093 & 0.875$\pm$0.112 & 0.918$\pm$0.113 \\
U-Net++  \cite{zhou2018unetnestedunetarchitecture} & 0.917$\pm$0.105 & 0.859$\pm$0.127 & 0.882$\pm$0.130 \\
W-Net  \cite{xia2017wnetdeepmodelfully}   & 0.913$\pm$0.123 & 0.856$\pm$0.153 & 0.897$\pm$0.165  \\
Neuralizer  \cite{neuralizer}     & 0.859$\pm$0.104 & 0.764$\pm$0.118 & 0.882$\pm$0.119  \\
\midrule
\textbf{ICL-NoiseUNet }    & \textbf{0.965} $\pm$ 0.090 & \textbf{0.947} $\pm $0.022 & \textbf{0.973} $\pm $0.105  \\
\bottomrule
\end{tabular}
}
\end{table}
