\begin{table}[t]
\fontsize{7}{8}\selectfont
	\centering
	\caption{[MRNetData Dataset] Results of baseline methodologies without and with \myalgoname\ on different diagnosis tasks. Best setups, per task and per method, are highlighted in bold.}
	\label{tab:mrnetsota}
	\begin{tabular}{c | c | c c | c c}
		\toprule
		\multirow{2}{*}{Diagnosis Task} & \multirow{2}{*}{Metric} & \multicolumn{4}{c}{Architecture} \\
		 & & MRNet & with \myalgoname & ELNet & with \myalgoname \\
		\midrule
		 \multirow{4}{*}{ACL tear} & ROC-AUC & 0.955 \stddev{0.005} & \tblbest{0.976} \stddev{0.003} & 0.940 \stddev{0.001} & \tblbest{0.960} \stddev{0.015} \\
        	                    & Accuracy & 0.847 \stddev{0.005} & \tblbest{0.886} \stddev{0.010} & 0.808 \stddev{0.000} &  \tblbest{0.881} \stddev{0.034}\\
    	                        & Sensitivity & 0.722 \stddev{0.000} & \tblbest{0.815} \stddev{0.019} & 0.648 \stddev{0.019} & \tblbest{0.827} \stddev{0.039} \\
    	                        & Specificity & \tblbest{0.950} \stddev{0.009} & 0.944 \stddev{0.009} & \tblbest{0.939} \stddev{0.015} & 0.924 \stddev{0.030}\\
    	  
    	\midrule
    	
    	\multirow{4}{*}{Meniscus tear} & ROC-AUC & 0.843 \stddev{0.016} & \tblbest{0.889} \stddev{0.006} & 0.869 \stddev{0.031} & \tblbest{0.895} \stddev{0.008} \\
        	                    & Accuracy & 0.778 \stddev{0.027} & \tblbest{0.808} \stddev{0.008} & \tblbest{0.775} \stddev{0.044} & 0.761 \stddev{0.042} \\
    	                        & Sensitivity & 0.750 \stddev{0.067} & \tblbest{0.853} \stddev{0.048} & 0.814 \stddev{0.109} & \tblbest{0.872} \stddev{0.106} \\
    	                        & Specificity & \tblbest{0.799} \stddev{0.009} & 0.775 \stddev{0.052} & \tblbest{0.745} \stddev{0.075} &  0.676 \stddev{0.149} \\
    	                           
    		                          
    	
        \bottomrule
\end{tabular}
\end{table}