提出集合式方法，用单向量高效表示病理切片图像。
A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology
- 基于集合的聚合技术，将全切片图像压缩为单一向量。
- 解决大尺寸图像计算效率与存储成本难题。
- 适合关注病理图像压缩与高效分析的研究者。
数字病理学通过将组织样本以全切片图像（WSI）形式数字化，推动了病理学的发展。这些图像可达千兆像素级别，包含丰富的组织细节，可用于诊断与研究。然而，由于其巨大尺寸，将WSI表示为紧凑向量对计算病理任务（如搜索与检索）至关重要，以确保效率与可扩展性。现有大多数方法采用“基于补丁”的策略，将图像分割为小块处理，无法实现整幅切片的全局分析。同时，高成本的高性能存储需求也限制了医院的普及，造成医疗资源不平等。本文综述了现有的基于集合的单向量WSI表示方法，强调其在提升复杂图像利用效率方面的创新，有效应对计算挑战与存储限制。
原文摘要 · Abstract (English)
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.
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