ArticleNature communications2025
Adaptive compression framework for giga-pixel whole slide images.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Accelerating the implementation of digital pathology in India.Journal of pathology informatics · 2026Review
- Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer.Scientific reports · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
Digital pathology generates gigapixel whole-slide images that require extensive storage, limiting scalability and increasing costs. Traditional compression methods apply uniform ratios, disregarding variations in diagnostic importance across regions. Here we show Adaptive compression for gigapixel whole-slide images (AdaSlide), a framework that balances compression efficiency and diagnostic integrity. AdaSlide integrates a reinforcement learning-based Compression Decision Agent (CDA) that determines the optimal compression ratio per region, and a Foundational Image Enhancer (FIE) that restores visual fidelity after compression. In evaluations, pathologists achieved near-chance accuracy (55%) in distinguishing original from restored images, confirming high perceptual fidelity. Across 13 downstream tasks, AdaSlide maintained diagnostic performance in most tasks while reducing storage to 10-35% of the original size. By aligning compression strategies with clinical relevance, AdaSlide enables efficient, scalable, and reliable storage for digital pathology and future AI development.
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Registered trials
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