SPLICE -- Streamlining Digital Pathology Image Processing

TL;DR

SPLICE is an unsupervised algorithm that efficiently condenses large histopathology images into representative patches, reducing storage and computation needs while improving search accuracy in digital pathology.

Contribution

The paper introduces SPLICE, a novel unsupervised patching method that creates compact, non-redundant representations of whole slide images for faster and more efficient analysis.

Findings

Reduces storage requirements by 50%

Improves search and match accuracy

Decreases computation time for image processing

Abstract

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there's a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting non-redundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.