Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
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 report...
| Authors | Han Li, Jingsong Liu, Ayako Ura et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 17 July 2026 |
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- SchuefflerLab/TUM_UteriaDataset · cited by
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- SchuefflerLab/TUM_UteriaDataset
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