首个配对子宫全切片图像与病理报告的数据集，助力AI辅助诊断。
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
- 构建子宫全切片图像与病理报告的成对数据集
- 含216例临床病例、455个切片级图像-报告对
- 经多位病理专家验证，适合多模态病理研究
子宫疾病是妇科病理的重要类别，需精准组织病理评估以指导诊疗。全切片图像（WSI）推动了病理工作流程的数字化，并为计算病理学中的人工智能应用提供了新机遇。特别是联合分析组织病理图像与病理报告的多模态模型，在自动化报告生成和AI辅助诊断方面展现出潜力。然而，这类系统的发展受限于缺乏将全切片图像与具有临床意义的病理报告配对的数据集。现有病理数据集多聚焦于单个终点的片段或切片级标注（如疾病分类），难以完整捕捉完整临床诊断报告中的丰富信息。本文介绍TUM-Uteria，一个来自三级医疗中心的子宫病理数据集，包含在病例和切片层级上配对的全切片图像与诊断报告。该数据集共包含216个临床病例，455个切片级的WSI-报告配对，经过结构化的多阶段验证流程，由持证病理专家参与确保标注可靠性。TUM-Uteria支持计算病理学研究，包括全切片图像分析、多模态学习及自动化病理报告生成。
原文摘要 · Abstract (English)
Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment planning. Whole-slide images (WSI) have enabled the digital transformation of pathology workflows and provided new opportunities for artificial intelligence (AI) in computational pathology. In particular, multimodal models that jointly analyze histopathology images and pathology reports have shown promising potential for automated pathology report generation and AI-assisted diagnosis. However, the development of such systems remains limited by the scarcity of datasets that pair whole-slide images with clinically meaningful pathology reports. Instead, existing pathology datasets focus on patch- or slide-level annotations of a single endpoint (e.g., disease class), which do not fully capture the rich information in full clinical diagnostic workflow reports. Here, we introduce TUM-Uteria, a uterine pathology dataset comprising WSIs paired with diagnostic pathology reports at both the case and slide levels, collected from a tertiary medical center. The dataset contains 216 clinical cases, comprising 455 slide-level WSI-report pairs. The dataset underwent a structured multi-stage validation procedure involving board-certified pathologists to ensure reliable annotations. TUM-Uteria supports research in computational pathology, including whole-slide image analysis, multimodal learning, and automated pathology report generation.
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