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.
Abstract (translated)
数字病理学和解剖学与人工智能（AI）模型的结合已经推动了病理学的发展，带来了新的机遇。随着 whole slide images（WSIs）可用性的增加，对从广泛的生物医学档案中检索相关图像的高效检索、处理和分析的需求不断增加。然而，由于 WSIs 的较大尺寸和复杂性，处理 WSIs 存在挑战。完全计算机消化 WSIs 是不切实际的，而单独处理每个补丁的开销是昂贵的。在本文中，我们提出了一个自适应补丁算法：顺序补丁 lattice for image classification and enquiry（SPLICE）。这种新颖的方法将病理学 WSI 压缩成一系列具有代表性的补丁，形成了一个“拼贴画”式的 WSI，同时最小化冗余。SPLICE 通过顺序分析 WSI 和选择非冗余的代表性特征来优先考虑补丁的质量和大。我们对 SPLICE 进行了搜索和匹配应用的评估，证明了与现有最先进方法相比，准确率得到提高、计算时间得到缩短，存储要求降低。作为无监督的方法，SPLICE 有效地将存储WSI 的需求降低了50%。这一降低使得许多计算病理学算法能够更有效地运行，为加速数字病理学的发展铺平道路。
URL
https://arxiv.org/abs/2404.17704
