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English(EN) Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology

新数据集将子宫病理图像与报告配对,用于人工智能研究

研究人员推出了 TUM-Uteria,这是一个旨在推进计算病理学多模态学习的新数据集。该数据集将子宫组织的全切片图像与相应的诊断病理报告配对,解决了此类配对数据严重短缺的问题。TUM-Uteria 包含 216 个临床病例,拥有 455 个切片级 WSI-报告对,并经过认证病理学家的验证,以支持人工智能辅助诊断和自动化报告生成方面的研究。 AI

影响 能够开发人工智能工具,用于子宫病理学中更准确的诊断和自动化报告。

排序理由 该集群包含一篇研究论文,详细介绍了用于计算病理学人工智能研究的新数据集。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新数据集将子宫病理图像与报告配对,用于人工智能研究

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该集群包含一篇研究论文,详细介绍了用于计算病理学人工智能研究的新数据集。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Han Li, Jingsong Liu, Ayako Ura, Junlin Hou, Zhengyang Xu, Azar Kazemi, Oskar Thaeter, Christian Grashei, Fabian G\"ulhan, Reza Nasirigerdeh, Xun Ma, Rui Yan, Hao Chen, S. Kevin Zhou, Nassir Navab, Carolin Mogler, Peter Sch\"uffler ·

    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

    arXiv:2607.04020v1 Announce Type: new 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 pa…