数字病理学中用于单向量 WSI 表示的基于集合的聚合技术简短综述
A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology
AI总结：
本文综述数字病理学中基于集合的单向量 WSI 表示方法，以克服补丁式处理的局限并降低高性能存储需求。
摘要：
数字病理学正在通过将组织样本以全切片图像（WSIs）的形式进行数字化、存储和分析，变革病理学领域。WSI 是能够捕捉组织样本复杂细节的千兆像素文件，为诊断和研究目的提供了丰富的信息来源。然而，由于其尺寸巨大，将这些图像表示为一个紧凑向量对于许多计算病理学任务（例如搜索与检索）至关重要，以确保效率和可扩展性。当前大多数方法都是“面向补丁的”，这意味着它们将 WSI 划分为较小的补丁进行处理，从而阻碍了对整张切片的整体分析。此外，对紧凑表示的需求还来自 WSI 所需的昂贵高性能存储。并非所有医院都能获得此类大规模存储解决方案，这会导致医疗质量和可及性方面的潜在差距。本文概述了现有的用于单向量 WSI 表示的基于集合的方法，重点介绍了能够在数字病理学中更高效、更有效地使用这些复杂图像的创新，从而同时应对计算挑战和存储限制。
英文摘要：
Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. However, due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks, such as search and retrieval, to ensure efficiency and scalability. Most current methods are "patch-oriented," meaning they divide WSIs into smaller patches for processing, which prevents a holistic analysis of the entire slide. Additionally, the necessity for compact representation is driven by the expensive high-performance storage required for WSIs. Not all hospitals have access to such extensive storage solutions, leading to potential disparities in healthcare quality and accessibility. This paper provides an overview of existing set-based approaches to single-vector WSI representation, highlighting the innovations that allow for more efficient and effective use of these complex images in digital pathology, thus addressing both computational challenges and storage limitations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
