{"ID":2828573,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.14796","arxiv_id":"2512.14796","title":"Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images","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","short_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 neuro...","url_abs":"https://arxiv.org/abs/2512.14796","url_pdf":"https://arxiv.org/pdf/2512.14796v1","authors":"[\"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\"]","published":"2025-12-16T15:47:45Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}