\begin{table*}[t]
\small
\caption{Dice (\%) for ablation study on kidney dataset with 10\% labeled data. AU, EU: aleatoric/epistemic uncertainty. JPS: joint pseudo-label supervision. ML-D: mutual learning in decoder. ML-EB: mutual learning in encoder and bottleneck.} 

\label{ablation_table}
\begin{center}
    \begin{tabular}{ l  | c |c |c |c | c |c }    
    \toprule

    % \multicolumn{1}{l}{Labeled} &  
    % \multicolumn{1}{c|}{baseline \cite{chen2021semi}} & 
    % \multicolumn{1}{|c}{+ AU.} &  
    % \multicolumn{1}{|c}{+ EU.} &
    % \multicolumn{1}{|c}{+ JPS} &
    % \multicolumn{1}{|c}{+ ML-D} &
    % \multicolumn{1}{|c}{+ ML-E}
    %\\
 
    & Baseline \cite{chen2021semi}
    & + AU
    & + EU
    & + JPS
    & + ML-D
    & + ML-EB
    \\
    
    \hline
    Endo-SemiS-1 
    & 85.2 % baseline
    & 86.2 % strong-weak augmentation
    & 86.9 % (uncertainty)
    & \textbf{87.8} % joint learning
    & 87.2 % mutual learning for decoder only
    & 87.6 % mutual learning
    \\
    \hline
    Endo-SemiS-2 
    & 84.5 % baseline
    & 86.4 % strong-weak augmentation
    & 87.2 % (uncertainty)
    & 86.8 % joint learning
    & 87.5 % mutual learning for decoder only
    & \textbf{87.6} % mutual learning
    \\
    
    \bottomrule

\end{tabular} 

\end{center}
\end{table*}




