Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient matching. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets ($1269$ patients) and three public datasets ($1207$ patients), totaling more than $200,000$ patches from $38$ different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.

翻译：在组织学和组织病理学图像档案中搜索相似图像是一项关键任务，可能有助于患者匹配，用于从分诊、诊断到预后和预测等多种目的。全切片图像（WSI）是载玻片上组织标本的高细节数字表示。将WSI与WSI匹配可作为患者匹配的关键方法。在本文中，我们报告了对四种搜索方法（视觉词袋（BoVW）、Yottixel、SISH、RetCCL）及其部分潜在变体的广泛分析与验证。我们分析了它们的算法和结构，并评估了它们的性能。为此评估，我们使用了四个内部数据集（1269名患者）和三个公共数据集（1207名患者），总计超过20万个来自五个主要部位、38个不同类别/子类型的图像块。某些搜索引擎，例如BoVW，表现出显著的效率和速度，但准确性较低。相反，像Yottixel这样的搜索引擎兼具效率和速度，提供了中等准确的结果。最近的提案（包括SISH）效率低下且结果不一致，而RetCCL等替代方案在准确性和效率方面均显不足。进一步的研究对于解决组织病理学图像搜索中准确性与最小存储需求的双重方面至关重要。