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unet/tables/comprehensive_results.csv

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+Scenario,Accuracy,Precision,Recall,Specificity,Dice,IoU,HD95,ASSD
+Binary Classification (Processed),0.9852170766510578,0.334045083283424,0.968163922466384,0.999754768925667,0.49671043079160454,0.33041566610336304,,
+Three-class Classification (Processed),0.9964570229097923,0.7588945440239975,0.776468220525528,0.9983026668503032,0.7675808086160567,0.6228244304656982,,
+Statistical Analysis,Dice p=0.0000,Dice t=9.6244,Dice Δ=0.1702,Dice ES=0.5643,IoU p=0.0000,IoU Δ=0.1683,HD95 Δ=4.0886px,ASSD Δ=-3.0984px

unet/tables/latex_table.tex

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+\begin{table}
+\caption{Performance comparison between binary and three-class segmentation approaches}
+\label{tab:performance_comparison}
+\begin{tabular}{lllllllll}
+\toprule
+Scenario & Accuracy & Precision & Recall & Specificity & Dice & IoU & HD95 & ASSD \\
+\midrule
+Binary Classification (Processed) & 0.9852 & 0.3340 & 0.9682 & 0.9998 & 0.4967 & 0.3304 & NaN & NaN \\
+Three-class Classification (Processed) & 0.9965 & 0.7589 & 0.7765 & 0.9983 & 0.7676 & 0.6228 & NaN & NaN \\
+\bottomrule
+\end{tabular}
+\end{table}