% \begin{table*}[t]
% \small
% \caption{Quantitative results (\%) in polyp dataset with 10\% labeled data. The left and right parts show the results for single and sequence frames, respectively. Cross Teaching-2 denotes the Swin transformer. Bold indictaes the \textbf{best} Dice. Underline represents the \underline{second best}. Lavender marks the cross-supervised methods. ``Upper bound" methods trained with 100\% labeled data.} 
% \label{polyp_table_rebuttal}
% \begin{center}
% \begin{tabular}{ l  | c |c |c |c |c |c }
% \toprule
% \multicolumn{1}{l}{} &  \multicolumn{3}{c}{Single frame data} & \multicolumn{3}{c}{Sequence frame data} \\
% \hline
% \multicolumn{1}{l}{Methods} &  \multicolumn{1}{c}{Dice} & \multicolumn{1}{c}{Sensitivity} & \multicolumn{1}{c|}{Specificity} &  \multicolumn{1}{c}{Dice} & \multicolumn{1}{c}{Sensitivity} & \multicolumn{1}{c}{Specificity}\\

% \midrule

% U-Net & 75$\pm$30 & 73$\pm$31 & 100$\pm$1 & 64$\pm$38 & 64$\pm$38 & 100$\pm$1 \\
% nnUNet &  &  &  &  &  &  \\
% PNS+ & 71$\pm$34 & 71$\pm$35 & 99$\pm$2 & 48$\pm$41 & 47$\pm$42 & 99$\pm$3 \\
% DSHNet & 80$\pm$24 & 87$\pm$24 & 99$\pm$1 & 59$\pm$39 & 76$\pm$31 & 99$\pm$2 \\

% \midrule
% \rowcolor{crosssup}
% CPS-1 & 77$\pm$32 & 74$\pm$33 & 100$\pm$1 & 68$\pm$40 & 65$\pm$40 & 100$\pm$1 \\
% \rowcolor{crosssup}
% CPS-2 & 75$\pm$34 & 71$\pm$34 & 100$\pm$1 & 64$\pm$42 & 65$\pm$42 & 100$\pm$1 \\
% \rowcolor{crosssup}
% Cross Teaching-1 & 73$\pm$35 & 70$\pm$36 & 100$\pm$1 & 63$\pm$41 & 60$\pm$41 & 100$\pm$1 \\
% \rowcolor{crosssup}
% Cross Teaching-2 & 75$\pm$34 & 73$\pm$34 & 100$\pm$1 & 65$\pm$40 & 63$\pm$40 & 100$\pm$1 \\
% \rowcolor{crosssup}
% Endo-SemiS-1 & 76$\pm$34 & 75$\pm$34 & 100$\pm$1 & 69$\pm$39 & 67$\pm$39 & 100$\pm$1 \\
% \rowcolor{crosssup}
% Endo-SemiS-2 & 79$\pm$30 & 77$\pm$31 & 100$\pm$1 & \underline{71$\pm$37} & 70$\pm$37 & 100$\pm$2 \\
% \rowcolor{crosssup}
% Endo-SemiS-1 (DSHNet) & \underline{80$\pm$28} & 82$\pm$29 & 100$\pm$1 & \textbf{73$\pm$34} & 73$\pm$34 & 99$\pm$1 \\
% \rowcolor{crosssup}
% Endo-SemiS-2 (DSHNet) & 78$\pm$30 & 82$\pm$29 & 99$\pm$1 & \textbf{73$\pm$35} & 73$\pm$34 & 100$\pm$1 \\

% \midrule

% Upper bound U-Net & 79$\pm$30* & 79$\pm$31 & 99$\pm$2 & 69$\pm$37* & 74$\pm$35 & 99$\pm$2 \\
% Upper bound nnUNet &  &  &  &  &  &  \\
% Upper bound PNS+ & 74$\pm$32 & 77$\pm$33 & 99$\pm$3 & 49$\pm$42 & 49$\pm$43 & 99$\pm$3 \\
% Upper bound DSHNet & \textbf{85$\pm$24} & 92$\pm$19 & 99$\pm$2 & 66$\pm$39 & 79$\pm$33 & 99$\pm$3 \\
% \bottomrule
% \multicolumn{7}{l}{
% \begin{tabular}{@{}l@{}@{}}
% * denotes our implementation; benchmark \cite{ali2023multi} results are 79\% and 66\%.
% \end{tabular}
% } \\
% \end{tabular}
% \end{center}
% \end{table*}






\begin{table*}[t]
\small
\caption{Quantitative results in polyp dataset with 10\% labeled data. \colorbox{bestcell}{\textbf{Green}} and \colorbox{secondcell}{\textbf{yellow}} denote the best and second best mean Dice, respectively. Lavender denotes the cross-supervised methods. Endo-SemiS is model-agnostic and shows strong performance across diverse backbones with only 10\% labeled data, particularly on sequence data that better reflects real-world endoscopic procedures.}
\label{polyp_table_rebuttal}
\begin{center}
\setlength{\tabcolsep}{3.5pt}
\renewcommand{\arraystretch}{1.05}
\begin{tabular}{ l | c c c | c c c }
\toprule
 & \multicolumn{3}{c|}{Single frame data} & \multicolumn{3}{c}{Sequence frame data} \\
\cline{2-7}
Methods & Dice & Sensitivity & Specificity & Dice & Sensitivity & Specificity \\
\midrule
U-Net & 75$\pm$30 & 73$\pm$31 & 100$\pm$1 & 64$\pm$38 & 64$\pm$38 & 100$\pm$1 \\
nnUNet & 75$\pm$33 & 78$\pm$32 & 99$\pm$3 & 53$\pm$43 & 68$\pm$40 &  99$\pm$4\\

PNS+ & 71$\pm$34 & 71$\pm$35 & 99$\pm$2 & 48$\pm$41 & 47$\pm$42 & 99$\pm$3 \\
DSHNet & \second{\textbf{80$\pm$24}} & 87$\pm$24 & 99$\pm$1 & 59$\pm$39 & 76$\pm$31 & 99$\pm$2 \\
EfficientNet & 67$\pm$33 & 69$\pm$35 & 99$\pm$2 & 44$\pm$39 & 41$\pm$37 & 99$\pm$2 \\




\midrule
\rowcolor{crosssup}
CPS-1 (UNet) & 77$\pm$32 & 74$\pm$33 & 100$\pm$1 & 68$\pm$40 & 65$\pm$40 & 100$\pm$1 \\
\rowcolor{crosssup}
CPS-2 (UNet) & 75$\pm$34 & 71$\pm$34 & 100$\pm$1 & 64$\pm$42 & 65$\pm$42 & 100$\pm$1 \\
\rowcolor{crosssup}
Cross Teaching-1 (UNet) & 73$\pm$35 & 70$\pm$36 & 100$\pm$1 & 63$\pm$41 & 60$\pm$41 & 100$\pm$1 \\
\rowcolor{crosssup}
Cross Teaching-2 (UNet) & 75$\pm$34 & 73$\pm$34 & 100$\pm$1 & 65$\pm$40 & 63$\pm$40 & 100$\pm$1 \\
\rowcolor{crosssup}
Endo-SemiS-1 (UNet) & 76$\pm$34 & 75$\pm$34 & 100$\pm$1 & 69$\pm$39 & 67$\pm$39 & 100$\pm$1 \\
\rowcolor{crosssup}
Endo-SemiS-2 (UNet) & 79$\pm$30 & 77$\pm$31 & 100$\pm$1 & \second{\textbf{71$\pm$37}} & 70$\pm$37 & 100$\pm$2 \\
\midrule
\rowcolor{crosssup}
Endo-SemiS-1 (DSHNet) & \second{\textbf{80$\pm$28}} & 82$\pm$29 & 100$\pm$1 & \best{73$\pm$34} & 73$\pm$34 & 99$\pm$1 \\
\rowcolor{crosssup}
Endo-SemiS-2 (DSHNet) & 78$\pm$30 & 82$\pm$29 & 99$\pm$1 & \best{73$\pm$35} & 73$\pm$34 & 100$\pm$1 \\
\midrule
\rowcolor{crosssup}
Endo-SemiS-1 (EfficientNet) & 73$\pm$31 & 81$\pm$32 & 99$\pm$2 & 70$\pm$36 & 73$\pm$35 &  99$\pm$2 \\
\rowcolor{crosssup}
Endo-SemiS-2 (EfficientNet) & 74$\pm$32 & 79$\pm$33 & 99$\pm$2 & \second{\textbf{71$\pm$36}} & 71$\pm$36 & 99$\pm$2 \\
\midrule



Upper bound U-Net & 79$\pm$30* & 79$\pm$31 & 99$\pm$2 & 69$\pm$37* & 74$\pm$35 & 99$\pm$2 \\
Upper bound nnUNet & \second{\textbf{80$\pm$29}} & 84$\pm$26 & 99$\pm$1 & 62$\pm$41 & 71$\pm$38 & 99$\pm$2 \\

Upper bound PNS+ & 74$\pm$32 & 77$\pm$33 & 99$\pm$3 & 49$\pm$42 & 49$\pm$43 & 99$\pm$3 \\
Upper bound DSHNet & \best{85$\pm$24} & 92$\pm$19 & 99$\pm$2 & 66$\pm$39 & 79$\pm$33 & 99$\pm$3 \\
Upper bound EfficientNet & 75$\pm$32 & 77$\pm$34 & 99$\pm$1 & 45$\pm$40 & 41$\pm$40 & 100$\pm$1 \\
\bottomrule
\multicolumn{7}{l}{
\begin{tabular}{@{}l@{}}
* denotes our implementation; benchmark \cite{ali2023multi} results are 79\% and 66\%.
\end{tabular}
} \\
\end{tabular}
\end{center}
\end{table*}
