Spatial quantification is essential in computational pathology, yet many multiple-instance learning methods gain slide-level accuracy at the cost of spatial awareness. SMMILe shows that instance-level aggregation can achieve strong spatial quantification without sacrificing whole-slide prediction and introduces a superpatch-based measurable MIL formulation. Across multiple cancer types, tasks, and datasets, it consistently improves spatial localization and slide-level performance.