从幻灯片到AI-ready地图：标准化的多层组织地图作为数字病理学中人工智能的元数据
From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology
AI总结：
本文提出了一种生成标准化多层组织地图的框架，用于数字病理学中AI的元数据标准化，提升WSI档案的搜索能力和数据集构建效率。
摘要：
全滑片图像（WSI）是通过多倍放大扫描整个玻璃载玻片（包含生物样本，如组织切片或细胞样本）生成的高分辨率数字图像。这些图像可以数字化查看、分析和共享，并广泛用于人工智能（AI）算法开发。WSI在病理学中用于疾病诊断，在肿瘤学中用于癌症研究，但也应用于神经学、兽医学、血液学、微生物学、皮肤病学、药理学、毒理学、免疫学和法医学。在为AI训练或验证构建队列时，了解WSI的内容至关重要。然而，目前尚无标准的元数据规范，此类选择大多依赖手动检查，这不适用于拥有数百万对象的大型集合。我们提出了一种通用框架，用于生成描述WSI形态内容的2D索引地图（组织地图），使用通用语法和语义以实现目录之间的互操作性。组织地图分为三个层次：来源、组织类型和病理改变。每个层次将WSI片段分配到特定类别，提供AI-ready的元数据。我们通过应用基于AI的元数据提取从WSI生成组织地图，并将其整合到WSI档案中。这种整合增强了WSI档案内的搜索能力，从而促进高质量、平衡且更针对性的数据集的快速构建，用于AI训练、验证和癌症研究。
英文摘要：
A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.
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。
