## 1 Introduction  
According to GLOBOCAN 2022, nearly 20 million new cancer cases were diagnosed, and 9.7 million fatalities were reported [1]. The article also estimated that 20% of people develop cancer in their lifetime. Lung and female breast cancers were among the most frequently reported cases, accounting for 12.4% and 11.6% of all cancer patients that year, respectively. If the current trend continues, an estimated 35 million cases will be identified by 2050. [PERSON] et al. [2] analyzed the cancer profiles based on the reports published during 2020 to 2022. They observed a notable increase in thyroid cancers, whilst cases and fatality rates of both stomach and esophageal cancers showed a decline. Although cancer remains a leading health concern, advancements in treatment and prevention efforts play a crucial role in reducing its impacts. However, their Mortality-to-Incidence Ratio (MIR) vary notably across countries with differing socioeconomic statuses. The surveys highlight that breast cancer (BCa) is a most fatal type, severely impacting women's health worldwide. Invasive ductal carcinoma (IDC) is the most common type of BCA, accounting for about 80% of all breast cancers [3, 4]. IDC both affects patients' quality of life and contributes to higher MIR. Once IDC originated, its extensive genetic and molecular diversity facilitates rapid spread into adjacent breast tissues [5]. The intratumor heterogeneity observed in IDC complicates disease progression, posing challengesfor therapeutic strategies and patient outcomes. Current research prioritizes the development of technologies for diagnostics and interventions. Effective interdisciplinary studies, such as genomic profiling, computerized subtype prediction, and immunomodulatory intervention [4; 5; 6; 7], could drive management strategies and enhance treatment responses. In addition to the above interdisciplinary efforts, artificial intelligence (AI) has also recently played a pivotal role in computer-aided diagnosis (CAD) [8; 9; 10]. Computer vision for modern medicine has been driven by Machine Learning (ML) and Deep Learning (DL) models, enabling precise identification and characterization of biomarkers and pathological features. A convolutional neural network (CNN) is an Artificial Neural Network (ANN) that has been widely adopted in recent CAD research, due to their accessible and versatile software libraries [11; 12]. Prominent CNN architectures, e.g., DenseNet [13], VGG [14], and ResNet [15], have proven effective in identifying abnormality and disease classification in medical images. By extracting deep features and associating them with known pathological patterns, these decision-support models can assist physicians in reducing diagnostic errors and optimizing treatment outcomes [16; 17; 18]. Building on these advancements, AI in digital health is set to address socioeconomic gaps that continue to hinder cancer patient management [2]. Whole slide imaging (WSI) is recognized as the current gold standard for screening and serves as crucial component in digital pathology. It digitizes glass tissue slides at high resolution, preserving rich diagnostic information, enabling comprehensive coverage and detailed interpretation of pathological images [19]. When utilized in CAD, WSI is often divided into smaller patches for detailed analysis [4; 20; 21]. Although this modality has demonstrated significant value in cancer screening, its exceptionally large data size (e.g., 100 K \(\times\) 100 K pixels, corresponding to 0.25 \(\times\) 0.25 \(\upmu\)m\({}^{2}\)) presents challenges, requiring extensive archiving capability and considerable computational resources. In an AI-based system [22; 23], even problems involving moderately sized patches require hours of training and demand several gigabytes of Graphical Processing Unit (GPU) memory. Depending on the model complexity and the dataset being evaluated, accuracy ranges from about 75 to 95%. Therefore, care must be taken when deploying AI models in practical clinical settings to ensure patient safety.