Analysing whole slide images in digital pathology for disease detection and diagnosis is a challenge, as it requires balancing fine-grained details with broader tissue context. High-resolution images offer detailed information but often result in slow processing times, while lower-resolution images capture larger contextual areas at the cost of missing critical details. This study explores the research question of how to effectively balance these needs by proposing a cascade framework that integrates multiple resolution levels to optimize both accuracy and computational efficiency in detecting breast cancer metastasis using the CAMELYON16 dataset. Surprisingly, intermediate-resolution levels (10 \(\times \) magnification) outperformed the highest resolution (40 \(\times \) ), challenging conventional assumptions. Expanding the field-of-view during inference improved performance universally across all resolution levels without retraining. Our cascade pipeline selectively applies high-resolution analysis to regions flagged at lower resolutions. The optimal configuration, combining 5 \(\times \) screening with targeted 20 \(\times \) analysis, achieved a 0.661 FROC score, surpassing single-resolution models by 4.4% and reducing inference time by 12.4%. These findings suggest that strategic multi-resolution approaches can enhance both accuracy and efficiency in computational pathology, potentially accelerating clinical diagnoses without compromising detection reliability.