Adaptive compression framework for giga-pixel whole slide images.

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 dow

SUBMITTER: Lee J 

PROVIDER: S-EPMC12780017 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature