\vspace{-5pt}
\begin{table}[H]
\centering
\label{tab:few_shot}
\resizebox{0.8\columnwidth}{!}{%
\begin{tabular}{lcccccc}
\toprule
\multirow{2}{*}{\textbf{Model }} &
\multicolumn{3}{c}{\textbf{CAMUS}} &
\multicolumn{3}{c}{\textbf{BUS--BRA}} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
& Dice $\uparrow$ & IoU $\uparrow$ & $p$--value & Dice $\uparrow$ & IoU $\uparrow$ & $p$--value \\
\midrule
\textbf{ICL-NoiseUNet} 
& \textbf{0.940} & \textbf{0.891 } & -- 
& \textbf{0.911 } & \textbf{0.831} & -- \\
PANet \cite{panetfewshotimagesemantic}
& 0.886  & 0.789 & $\dagger$ 
& 0.801  & 0.679  & $\dagger$ \\
FiLM \cite{filmvisualreasoninggeneral}
& 0.926 & 0.879  & $\dagger$ 
& 0.867  & 0.781  & $\dagger$ \\
ICL-NoiseUNet (-nm,+cbn)
& 0.930  & 0.881  & $\dagger$
& 0.891  & 0.813  & $\dagger$ \\
\bottomrule
\end{tabular}
}
\caption{Comparison with current State-of-The-Art few-shot learning models and feature-wise conditioning mechanisms (nm: Noise Modulation Block, cbn: Conditional BatchNorm \cite{cbn}).  BUS-BRA \cite{WilfridoGomezFlores.2023} results are stratified by scanner. Significance levels: $\dagger$ $p < 0.001$. We compute the $p$-value for Dice score significance against ICL-NoiseUNet using Wilcoxon signed-rank pairwise tests with Bonferroni correction.}
\end{table}
