## 4 Results
### Model Analysis and Benchmarking  
For clarity, the model configured with \(\lambda\) = 4 and \(\alpha\) = 0.90, using CCE, as the loss function is referred to as Model A, while the model with \(\lambda\) = 16 and \(\alpha\) = 0.90, using BCE, is  
Figure 4: The Box-Wisker plots of the accuracy with different fusion strategies (**A**) and with different loss functions (**B**), at varying weights (\(\alpha\)). Figure 5: The accuracy achieved by models configured different weights (\(\alpha\)) and scales (\(\lambda\)), based on CCE (**A**) and BCE (**B**) loss functions. An asterisk marks the best value within each configuration. designated as Model B. The confusion matrixes given by Models A and B are depicted in Figures 6A and 6B, respectively. Each matrix is utilized to evaluate the performance of the respective model by summarizing its predictions against the actual classes. In the following reports, both models were evaluated across four independent runs, and their performance measures were averaged to ensure reliable interpretations. Deeper insights into the model effectiveness in distinguishing between IDC classes are shown in Figure 7A. The graph compares the accuracy, precision, recall, specificity, and F1-score between both models. Their overall accuracies were comparable to two decimal places. However, Model A demonstrated consistently higher on all metrics, especially precision. The ROCs in Figure 7B show that the AUC of Model A was marginally higher than Model B (0.8761 vs. 0.8716). While F1-score and AUC may exhibit similar trends for both models, these metrics assess different aspects of performance. Therefore, care should be taken when interpreting model effectiveness. Finally, the individual performance metrics of Model A and Model B were then compared with state-of-the-art models, with the same scale (\(\lambda\)), weights (\(\alpha\)), and loss functions, and are listed in Table 1 and Table 2, respectively. Figure 6: Confusion matrices achieved by Model A (\(\lambda=4\), \(\alpha=0.9\), CCE) (**A**) and Model B (\(\lambda=16\), \(\alpha=0.9\), BCE) (**B**). Numbers are expressed in percentages relative to each respective actual class. Figure 7: Comparisons of evaluation metrics distributions (**A**) and ROC curve (**B**) between Model A (\(\lambda=4\), \(\alpha=0.9\), CCE) and Model B (\(\lambda=16\), \(\alpha=0.9\), BCE). The proposed ResNet-50 model outperformed other architectures across all metrics, except for the specificity in Model A. In Model A, accuracy, precision, and F1-score showed statistically significant differences, while recall was notable but less significant, and specificity was not significant. In Model B, although ResNet-50 maintained the best overall performance, significance levels were generally lower compared to Model A, with only accuracy reaching statistical significance. The CAM of selected examples, one for IDC\(-\) and two IDC+ cases, obtained by Models A and B are depicted in Figures 8A and 8B, respectively. They visualize where each model focuses on a WSI when making their prediction. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{4}{c}{**Model A: \(\boldsymbol{\lambda}\) = 4 and \(\boldsymbol{\alpha}\) = 0.90 (CCE)**} \\ \hline  & **VGG-16** & **VGG-19** & **ResNet-50** & **DenseNet-121** & \(\boldsymbol{p}\)1 ** \\ \hline Accuracy & 0.7890 \(\pm\) 0.0051 & 0.7896 \(\pm\) 0.0087 & **0.8125 \(\pm\) 0.0069** & 0.7921 \(\pm\) 0.0059 & 0.003 \(\boldsymbol{}^{**}\) \\ Precision & 0.7938 \(\pm\) 0.0142 & 0.8063 \(\pm\) 0.0069 & **0.8179 \(\pm\) 0.0089** & 0.7921 \(\pm\) 0.0110 & 0.038 \(\boldsymbol{}^{**}\) \\ Recall & 0.7770 \(\pm\) 0.0236 & 0.7588 \(\pm\) 0.0335 & **0.8092 \(\pm\) 0.0126** & 0.7919 \(\pm\) 0.0112 & 0.084 \({}^{*}\) \\ Specificity & 0.8011 \(\pm\) 0.0218 & **0.8198 \(\pm\) 0.0171** & 0.8160 \(\pm\) 0.0126 & 0.7923 \(\pm\) 0.0152 & 0.219 \\ F1-Score & 0.7849 \(\pm\) 0.0058 & 0.7813 \(\pm\) 0.0151 & **0.8134 \(\pm\) 0.0066** & 0.7919 \(\pm\) 0.0062 & 0.005 \(\boldsymbol{}^{**}\) \\ \hline \end{tabular}
\end{table}