SPLICE -- Streamlining Digital Pathology Image Processing

• URL: http://arxiv.org/abs/2404.17704v1
• Date: Fri, 26 Apr 2024 21:30:36 GMT
• Title: SPLICE -- Streamlining Digital Pathology Image Processing
• Authors: Areej Alsaafin, Peyman Nejat, Abubakr Shafique, Jibran Khan, Saghir Alfasly, Ghazal Alabtah, H. R. Tizhoosh,
• Abstract summary: We propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE) SPLICE condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%.
• Score: 0.7852714805965528
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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