用于多模态计算病理学的配对子宫全切片图像和病理报告
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
浏览论文内容
中文总结 AI 辅助
研究子宫疾病病理诊断，针对全切片图像与病理报告配对数据集稀缺问题，引入TUM-Uteria数据集，含多对病例及切片级配对，经验证，为计算病理学研究提供支持。
中文摘要 AI 辅助
子宫疾病是妇科病理学的重要类别，全切片图像推动了病理学工作流程的数字化转型。联合分析组织病理学图像和病理报告的多模态模型在自动生成病理报告和人工智能辅助诊断方面显示出潜力。然而，此类系统的发展受到全切片图像与有临床意义的病理报告配对数据集稀缺的限制。我们引入了TUM-Uteria，这是一个子宫病理学数据集，包含从三级医疗中心收集的病例和切片级的全切片图像与诊断病理报告配对。该数据集包含216个临床病例，包括455个切片级全切片图像-报告对。该数据集经过了一个结构化的多阶段验证程序，涉及经董事会认证的病理学家，以确保可靠的注释。TUM-Uteria支持计算病理学的研究，包括全切片图像分析、多模态学习和自动病理报告生成。
英文摘要
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.
发表机构
- Institute of Pathology, Technical University of Munich（慕尼黑工业大学病理研究所）
- Computer Aided Medical Procedures (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序（CAMP））
- Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
- Department of Human Pathology, Juntendo University Graduate School of Medicine（顺天堂大学医学研究生院人体病理学部）
- The Hong Kong University of Science and Technology（香港科技大学）
- Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
