\begin{table}[htp]
    \centering
    \caption{\textbf{Camelyon16: Bag-Level Performance vs Instance-Level Interpretability.} Comparison of bag-level classification performance (AUC, F1) and instance-level interpretability measured by AUPC and AUPRC. AUPRC (Sig) is computed by applying a sigmoid to raw patch-level attentions.}
    \label{tab:C16-UNI}
    \centering
    \resizebox{0.9\linewidth}{!}{
    
    \begin{tabular}{lccccccc}
    \hline
    & \multicolumn{2}{c}{\textbf{Bag}}  &  \multicolumn{2}{c}{\textbf{Explainability}} &  \multicolumn{2}{c}{\textbf{Interpretability}} \\
    & \textbf{AUC} ($\uparrow$) 
    & \textbf{F1} ($\uparrow$)  
    &\textbf{AUPC} ($\downarrow$) 
    & \textbf{AOPCR} ($\uparrow$) 
    & \textbf{AUPRC$_{sig}$ }  ($\uparrow$)
    & \textbf{PG@5 }  ($\uparrow$)\\
    \hline
    Meanmil & 62.5 ± 4.8 & 46.7 ± 9.6 & N/A & N/A  & N/A & N/A \\
    MaxMIL & 98.3 ± 0.4 & 94.2 ± 1.9 & N/A & N/A & N/A & N/A \\
    DSMIL %~\cite{li2021dualstreammultipleinstancelearning} 
    & 98.9 ± 1.1 & 97.9 ± 1.1
    & 66.8 ± 44.8
    & 30.0 ± 40.2
    & 78.4 ± 9.7  
    & 20.8 ± 8.8  \\
    AddMIL %~\cite{javed2022additivemilintrinsicallyinterpretable} 
    & 98.2 ± 1.4 & 95.0 ± 3.4 
    & 67.5 ± 45.7
    & 28.4 ± 40.4
    & 93.4 ± 1.2 
    & 87.5 ± 0.0 \\
    MHIM %~\cite{mhim-mil} 
    & 98.2 ± 1.1 & 94.2 ± 2.8 
    & 72.9 ± 51.2
    & 27.8 ± 45.5
    & 93.2 ± 0.5
    & 86.1 ± 2.0 \\
    \hline
    ABMIL %~\cite{ilse2018attentionbaseddeepmultipleinstance} 
    & 98.7 ± 0.3 & 95.5 ± 2.2 
    & 69.6 ± 43.8
    & 25.3 ± 39.3
    & 93.2 ± 1.2
    & 86.8 ± 1.0 \\
    ABMIL-IBMIL & \textbf{99.9 ± 0.2} &  \textbf{99.3 ± 1.2}
    &  68.2 ± 42.7
    & 28.1 ± 38.9
    &  91.5 ± 2.4
    &  86.8 ± 1.0 \\
     \our \ours & \our 99.2 ± 0.5 & \our 96.3 ± 2.0 
    &  \our \underline{56.8 ± 23.8}
    & \our \underline{41.0 ± 22.7}
    & \our 92.9 ± 1.5
    &  \our 87.5 ± 0.0 \\
    
    \hline
    CLAM %~\cite{clam} 
    & \underline{99.7 ± 0.3} & 97.6 ± 2.1
    & 67.8 ± 44.4 
    & 27.9 ± 40.7
    & 94.4 ± 0.3
    & 87.5 ± 0.0 \\
    \our CIA-CLAM & \our 96.5 ± 6.1  & \our 93.0 ± 11.3
    &  \our \textbf{54.4 ± 26.2}
    & \our \textbf{42.7 ± 28.9}
    & \our 95.2 ± 0.2
    & \our 86.8 ± 1.0 \\
    
    \hline
     ACMIL %~\cite{zhang2024attentionchallengingmultipleinstancelearning} 
    & \underline{99.4 ± 0.5} & 96.9 ± 2.7
    & 68.2 ± 45.3
    & 28.1 ± 40.9
    & \underline{95.4 ± 0.5} 
    &  87.5 ± 0.0 \\
    
    \our CIA-ACMIL &  \our \textbf{99.9 ± 0.1} & \our \underline{98.6 ± 1.2}
    & \our 60.8 ± 32.0
    & \our 35.6 ± 29.5
    & \our \textbf{95.5 ± 0.4}
    & \our 87.5 ± 0.0 \\  
    \hline  

    \end{tabular}
    
    }
    
\end{table}