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arXiv · 2512.14796

Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images

Mahmut S. Gokmen·Mitchell A. Klusty·Peter T. Nelson·Allison M. Neltner·Sen-Ching Samson Cheung·Thomas M. Pearce·David A Gutman·Brittany N. Dugger·Devavrat S. Bisht·Margaret E. Flanagan·V. K. Cody Bumgardner
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Abstract

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 self-supervised strategy that links low-magnification context with spatially aligned high-magnification detail, enabling the model to learn how coarse tissue structure relates to fine cellular patterns. The resulting foundation model, MAD-NP, is trained entirely through this cross-scale correspondence without annotations. A linear classifier trained only on 10x embeddings maintains 96.7% of its performance when applied to unseen 40x tiles, demonstrating strong resolution-invariant representation learning. Segmentation outputs remain consistent across magnifications, preserving anatomical boundaries and minimizing noise. These results highlight the feasibility of scalable, magnification-robust WSI analysis using a unified embedding space

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Mahmut S. Gokmen, Mitchell A. Klusty, Peter T. Nelson, Allison M. Neltner, Sen-Ching Samson Cheung, Thomas M. Pearce, David A Gutman, Brittany N. Dugger, Devavrat S. Bisht, Margaret E. Flanagan, V. K. Cody Bumgardner. 2025-12-16. Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images. https://arxiv.org/abs/2512.14796

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