\begin{table}[htbp]
\centering
\caption{Results of 5 fold cross validation for the task NC vs AD. Format: Mean (standard deviation), best result in bold. The lower section consists of models with domain knowledge, while the upper section does not. All the models are composed of a ResNet18 and the AM module specified in the first column.}
\label{Tab: NC_AD_results}
\setlength\tabcolsep{2.6pt} % make LaTeX figure out intercolumn spacing
\renewcommand{\arraystretch}{1.5}
\begin{tabular}{cccccc}
\\
\toprule
    
    \textbf{Models} & {\textbf{AM Location}} & \textbf{ACC (\%)} & \textbf{SEN (\%)} & \textbf{SPE (\%)} & \textbf{$F_1$-score (\%)}  \\
    
    \midrule
    
      
    Standard ResNet18 & - & 95.2 (1.7) & 95.0 (2.4) & 95.3 (2.4) & 94.8 (1.9) \\ 
    
    CBAM & middle & 94.9 (2.0) & 94.7 (2.7) & 95.3 (2.7) & 94.6 (2.4) \\
    
    CBAM spatial module & middle & 95.0 (1.7) & 94.8 (3.1) & 95.2 (3.4) & 94.6 (2.4) \\
    
    Residual attention & throughout & 95.5 (2.2) & 94.7 (3.8) & \textbf{96.2 (2.3)} & 94.8 (2.4) \\
    
    \midrule
    
    Constant average map & middle &  94.8 (1.5) & 93.7 (3.4) & 95.9 (2.4) & 94.4 (1.5)\\
    
    ROI & start & \textbf{95.6 (2.6)} & \textbf{95.1 (2.5)} & 96.1 (2.8) & \textbf{95.2 (2.7)}\\
    
    Deep multiscale network & start & 94.0 (1.9) & 92.9 (4.0) & 94.9 (2.4) & 93.4 (2.8) \\
    
    U-Net & middle & 95.2 (2.1) & 94.7 (4.0) & 95.6 (2.2) & 94.6 (2.1) \\
    
\bottomrule
    
\end{tabular}
\end{table}



