Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology

Han Li; Jingsong Liu; Ayako Ura; Junlin Hou; Zhengyang Xu; Azar Kazemi; Oskar Thaeter; Christian Grashei; Fabian Gülhan; Reza Nasirigerdeh; Xun Ma; Rui Yan; Hao Chen; S. Kevin Zhou; Nassir Navab; Carolin Mogler; Peter Schüffler

arXiv · 2607.04020

Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology

Han Li·Jingsong Liu·Ayako Ura·Junlin Hou·Zhengyang Xu·Azar Kazemi·Oskar Thaeter·Christian Grashei·Fabian Gülhan·Reza Nasirigerdeh·Xun Ma·Rui Yan·Hao Chen·S. Kevin Zhou·Nassir Navab·Carolin Mogler·Peter Schüffler

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Abstract

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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Han Li, Jingsong Liu, Ayako Ura, Junlin Hou, Zhengyang Xu, Azar Kazemi, Oskar Thaeter, Christian Grashei, Fabian Gülhan, Reza Nasirigerdeh, Xun Ma, Rui Yan, Hao Chen, S. Kevin Zhou, Nassir Navab, Carolin Mogler, Peter Schüffler. 2026-07-17. Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology. https://arxiv.org/abs/2607.04020

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