Bone marrow smear analysis for childhood leukemia is labor-intensive and traditionally depends on detailed expert cell annotations. This work formulates the problem with patient-level supervision and introduces a hierarchical multi-instance learning framework enhanced by an information bottleneck. The model captures subtype relationships across multiple hierarchies and improves leukemia classification with better data efficiency and generalization.