This technology is an AI-driven image compression platform that intelligently reduces the size of digital pathology images while preserving diagnostically important information. Using machine learning and semantic tissue mapping, it identifies critical tissue regions and applies customized compression strategies that maintain image quality in clinically relevant areas while more aggressively compressing less informative regions. The result is a compressed image that remains visually equivalent to the original for diagnostic purposes but requires significantly less storage space and bandwidth.

Researchers, pathology laboratories, and healthcare systems can use this technology to address the growing data challenges associated with digital pathology and other imaging-intensive applications. By reducing file sizes without compromising diagnostic integrity, the technology lowers storage costs, accelerates image transfer and collaboration, enables more efficient cloud-based workflows, and supports the large-scale deployment of AI-powered diagnostic tools. This approach has the potential to improve workflow efficiency and scalability while making high-volume medical imaging data more manageable and accessible.

Technology Overview

Current Challenges

Healthcare systems are currently facing data burdens as digital pathology and other imaging-intensive specialties expand. Storage, bandwidth, and workflow capacity are overwhelmed with whole slide images (WSI) utilizing several gigabytes for a single tissue sample. Traditional compression tools were designed for general-purpose data and treat every pixel equally, leading to poor compression ratios and risks of degrading diagnostic content in a clinical setting. Maintaining clinically relevant files creates large storage costs and slow data transfer, directly impacting turnaround time, cloud migration, and the ability to use AI-based diagnostics at scale.

Our Innovation

This technology is an AI-driven system that intelligently compresses digital pathology images by preserving diagnostically important regions while optimizing storage and transmission efficiency. Utilizing artificial intelligence and machine learning, it identifies critical tissue regions within pathology images and applies varying compression levels to optimize file sizes without losing essential diagnostic information. The semantic encoder leverages tissue maps for identifying tissue types or pathologies to customize compression parameters. This system preserves diagnostic detail in important areas while compressing less significant regions more aggressively. The result is a compressed image that appears visually identical to the original for clinical purposes, while dramatically reducing storage and transmission requirements. This technology can be applied to other high-volume medical imaging applications aside from WSI.

Benefits of this Technology

  • Significantly reduces storage requirements for large pathology images
  • Preserves diagnostic accuracy by maintaining image quality in critical regions
  • Uses AI to intelligently identify and prioritize tissue areas based on pathology
  • Improves efficiency of image transmission and sharing

Stage of Development

The core components of the technology, including the semantic encoder, AI model, and image compression pipeline, have been successfully implemented. Preliminary validation has been demonstrated through three examples in the patent application showing before-and-after compressed images that maintain visual similarity to the original images, supporting the feasibility of the compression approach. Further optimization and performance evaluation may support future commercialization and application-specific deployment.