## 5 Discussion  
WSI is the gold standard in digital pathology for diagnosing BCa. Several studies have explored AI-based CAD systems designed to classify IDC pathology by using this imaging modality. Recent attempts have been either focusing on feature engineering [37], learning strategies [22; 23], or modifying network architecture [4]. The findings reported earlier this year [45] suggested that locally distilling knowledge from dual scale morphology could help enhance lesion identification and pathological annotation. Our research question aims to investigate whether morphologically enhanced filters (i.e., Gabor wavelet) can help improve the classification of IDC, potentially refining diagnostic accuracy. A ResNet-50 architecture with a modified dense layer was selected for its optimal balance between computation efficiency and prediction performance [16; 17; 21]. In the above experiments, only basic hyperparameter settings were made. The LR of 5.0 \(\times\) 10\({}^{-6}\) was identified as the most effective in achieving stable convergence for the given dataset. When comparing different fusion types, mean orientation invariant filters (\(R_{\text{Mean}}\)) demonstrated superior performance over the max filters (\(R_{\text{Max}}\)), regardless of weights (\(\alpha\)). This may be due to \(R_{\text{Max}}\) responses emphasizing cell boundaries excessively, while suppressing interior patterns, leading to greater degradation of morphological details compared to \(R_{\text{Mean}}\). Additionally, \(R_{\text{Mean}}\) more effectively captured spatial relationships between connecting structures across larger extents, due to accumulated responses in all directions. Rather than implementing formal dual scale distillation [45], we proposed fusing chromatic and morphological patterns via simple linear weighted combinations. The empirical results indicated that \(\lambda\) = 4 and \(\lambda\) = 16 were the most suitable scales for CCE and BCE loss function, respectively. It is worth noting that the weights remained 0.90 in both cases, suggesting that classification primarily relied on local chromatic distribution patterns, with additional support from enhanced boundary definitions. This aligns with the way pathologists interpret a histological slide, utilizing not only colors but also cellular arrangement and tissue morphology [24]. While ResNet's hidden neurons already capture the latter feature, augmenting it with Gabor responses clearly demonstrated its added contributions. This study employed linear combinations to progressively assess the impact of morphological enhancement on ResNet's performance. While effective, adaptive fusion could further improve results, as demonstrated by recent studies on adaptive convolutional kernel updates with Gabor templates [46] and adaptive Gabor filters [47] in image classification and recognition. Their results outline the potential directions for further refinement of the current method. A comparative analysis of both ResNet variants revealed that Model A demonstrated stronger performance in predicting IDC\(-\), primarily by minimizing falsely predicted IDC\(+\) (FP). This was reflected in its higher precision and specificity. However, both models exhibited similarly lower recall rates, indicating a common tendency to miss some IDC\(+\). In contrast, Model B classified both classes equally well, as evidenced in its more balanced recall and precision. This suggests that Model A, with its local focus (lower \(\lambda\)), was more effective in confirming IDC\(-\), while Model B (higher \(\lambda\)) was better suited to maintaining balance between sensitivity and specificity. The trade-off among these indicators underscores the complementary strengths of multi-band visual scales in diagnostic workflows. In other words, examining the slides at a broader scale can facilitate preliminary, unbiased screening, whereas finer-scale inspection may improve specificity by ruling out false IDC detection.