mlsanityData to 13 Oct 2026

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

arXiv 2512.14796 ↗First posted 16 Dec 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

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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References · 15

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3 more
  1. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 154
  2. 2020PanNuke Dataset Extension, Insights and Baselinescited by 113
  3. 2024PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysiscited by 7

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