Whole-slide images (WSIs) contain tissue information distributed across multiple magnification levels, yet most self-supervised methods treat these scales as independent views. This separation prevents models from learning representations that remain stable when resolution changes, a key requirement for practical neuropathology workflows. This study introduces Magnification-Aware Distillation (MAD), a framework that jointly processes multiple magnification levels to produce unified representations.
10 pages, 4 figures, 5 tables, submitted to AMIA 2026 Informatics Summit