\begin{table*}[ht]
\centering
\scriptsize
\setlength{\tabcolsep}{8pt}

\caption{\small 
Size-wise lesion analysis on the private MS dataset. 
\textbf{Left:} baseline lesion analysis. Lesion bins are defined using global 
tertile-based volume thresholds across the dataset 
(1\,mm\textsuperscript{3} isotropic voxels): 
Small \((\leq 84)\)\,mm\textsuperscript{3}, 
Medium \((84\text{--}208)\)\,mm\textsuperscript{3}, 
Large \((>208)\)\,mm\textsuperscript{3}. 
\textbf{Right:} analysis of new lesions in follow-up scans. Lesion bins use 
volume thresholds: 
Small \((\leq 43)\)\,mm\textsuperscript{3}, 
Medium \((43\text{--}113)\)\,mm\textsuperscript{3} and 
Large \((>113)\)\,mm\textsuperscript{3}.}
\label{tab:lesion_size_combined}

\begin{minipage}{0.475\textwidth}
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Model} & \textbf{Lesion-F1 $\uparrow$} & \textbf{Dice $\uparrow$} \\
\midrule

\multicolumn{3}{l}{\textit{Small} ($N=394$)} \\
DynUNet      & 0.176 & 0.076 \\
LongiUNet-DW & 0.186 & \textbf{0.216} \\
Swin UNETR   & \textbf{0.298} & 0.110 \\
SegFormer    & 0.139 & 0.087 \\
SegMaST (ours)     & 0.292 & 0.196 \\
\midrule

\multicolumn{3}{l}{\textit{Medium} ($N=388$)} \\
DynUNet      & 0.525 & 0.272 \\
LongiUNet-DW & 0.609 & 0.382 \\
Swin UNETR   & 0.699 & 0.345 \\
SegFormer    & 0.625 & 0.302 \\
SegMaST (ours)     & \textbf{0.746} & \textbf{0.424} \\
\midrule

\multicolumn{3}{l}{\textit{Large} ($N=392$)} \\
DynUNet      & 0.840 & 0.525 \\
LongiUNet-DW & 0.850 & 0.523 \\
Swin UNETR   & 0.891 & 0.601 \\
SegFormer    & 0.816 & 0.530 \\
SegMaST (ours)     & \textbf{0.915} & \textbf{0.617} \\
\bottomrule
\end{tabular}
\end{minipage}
\hspace{0.005\textwidth}
%
\begin{minipage}{0.475\textwidth}
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Model} & \textbf{Lesion-F1 $\uparrow$} & \textbf{Dice $\uparrow$} \\
\midrule

\multicolumn{3}{l}{\textit{Small} ($N=20$)} \\
DynUNet     & 0.034 & 0.010 \\
LongiNet-DW & 0.136 & 0.035 \\
Swin UNETR  & 0.000 & 0.000 \\
SegFormer   & 0.229 & \textbf{0.185} \\
SegMaST (ours)    & \textbf{0.235} & 0.097 \\
\midrule

\multicolumn{3}{l}{\textit{Medium} ($N=21$)} \\
DynUNet     & 0.081 & 0.172 \\
LongiNet-DW & 0.421 & 0.450 \\
Swin UNETR  & 0.376 & 0.309 \\
SegFormer   & 0.415 & 0.460 \\
SegMaST (ours)    & \textbf{0.703} & \textbf{0.598} \\
\midrule

\multicolumn{3}{l}{\textit{Large} ($N=21$)} \\
DynUNet     & 0.293 & 0.175 \\
LongiNet-DW & 0.871 & 0.612 \\
Swin UNETR  & 0.761 & 0.634 \\
SegFormer   & 0.746 & 0.683 \\
SegMaST (ours)    & \textbf{0.889} & \textbf{0.720} \\
\bottomrule
\end{tabular}
\end{minipage}

\end{table*}
