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Adaptive compression framework for giga-pixel whole slide images.

Jonghyun Lee1,2, Lina Takemaru1,3,4, D M Bappy2

  • 1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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Summary
This summary is machine-generated.

Adaptive compression for gigapixel whole-slide images (AdaSlide) reduces storage needs by 65-90% while maintaining diagnostic accuracy. This AI-powered framework optimizes compression for digital pathology images, enabling efficient storage and AI development.

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Area of Science:

  • Digital pathology
  • Medical imaging
  • Artificial intelligence

Background:

  • Gigapixel whole-slide images in digital pathology present significant storage challenges.
  • Traditional compression methods are inefficient as they apply uniform ratios, ignoring diagnostic importance of image regions.

Purpose of the Study:

  • To introduce Adaptive compression for gigapixel whole-slide images (AdaSlide), a novel framework for efficient and diagnostically sound image compression.
  • To balance compression efficiency with diagnostic integrity in whole-slide images.

Main Methods:

  • AdaSlide utilizes a reinforcement learning-based Compression Decision Agent (CDA) to determine region-specific compression ratios.
  • A Foundational Image Enhancer (FIE) is integrated to restore visual fidelity post-compression.

Main Results:

  • Pathologists could not reliably distinguish between original and AdaSlide-restored images (55% accuracy), indicating high perceptual fidelity.
  • Storage requirements were reduced to 10-35% of original size across various tasks.
  • Diagnostic performance was maintained across 13 downstream tasks.

Conclusions:

  • AdaSlide offers an efficient, scalable, and reliable solution for storing digital pathology images.
  • The framework aligns compression strategies with clinical relevance, supporting future AI development in pathology.