\begin{table}[htbp]
\centering
\caption{Comparison with state-of-the-art segmentation models on the BUS-BRA \cite{WilfridoGomezFlores.2023} dataset. All values are in percentage (\%). Results are averaged over $5$ runs.}
\label{tab:busbra_sota}
\resizebox{0.7\textwidth}{!}{%
\begin{tabular}{lccc}
\toprule
\textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{Dice} \\
\midrule
ResNet-50 \cite{he2015deepresiduallearningimage}     & 72.1 & 70.2 & 70.9 \\
SAM  \cite{kirillov2023segment}          & 80.1 & 78.3 & 79.1 \\
MedSAM      \cite{Ma.2024}    & 84.9 & 82.9 & 83.8 \\
UltraSAM    \cite{Meyer_2025}    & 85.5 & 85.3 & 85.8 \\
\midrule
\textbf{ICL-NoiseUNet (ours)} & \textbf{92.9$\pm$ 0.013} & \textbf{93.4$\pm$0.014} & \textbf{93.1$\pm$0.013} \\
\bottomrule
\end{tabular}
}
\end{table}
