Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images
Published:Dec 16, 2025 15:47
•1 min read
•ArXiv
Analysis
This article introduces a novel self-supervised framework, Magnification-Aware Distillation (MAD), for learning representations from gigapixel whole-slide images. The focus is on unified representation learning, which suggests an attempt to create a single, comprehensive model capable of handling the complexities of these large images. The use of self-supervision is significant, as it allows for learning without manual labeling, which is often a bottleneck in medical image analysis. The title clearly states the core contribution: a new framework (MAD) and its application to a specific type of image data (gigapixel whole-slide images).
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Reference
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