New dataset pairs uterine pathology images with reports for AI research

Researchers have introduced TUM-Uteria, a new dataset designed to advance multimodal learning in computational pathology. This dataset pairs whole-slide images of uterine tissue with corresponding diagnostic pathology reports, addressing a critical scarcity of such paired data. TUM-Uteria includes 216 clinical cases with 455 slide-level WSI-report pairs, validated by board-certified pathologists, to support research in AI-assisted diagnosis and automated report generation. AI

IMPACT Enables development of AI tools for more accurate diagnosis and automated reporting in uterine pathology.

RANK_REASON The cluster contains a research paper detailing a new dataset for AI research in computational pathology. [lever_c_demoted from research: ic=1 ai=1.0]

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• artificial intelligence
• arXiv
• Computational Pathology
• gynecologic pathology
• multimodal learning
• Pathologists' assistant
• TUM-Uteria

• paper
• other

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